



"""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 		_A       (			__lowerCamelCase				,       unittest.TestCase					):
      __a								=	UnCLIPImageVariationPipeline
      __a								=	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      __a								=	IMAGE_VARIATION_BATCH_PARAMS

      __a								=	[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      __a								=	False





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         return 32





      @property
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return 32





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         return self.time_input_dim





      @property
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         return self.time_input_dim * 4





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         return 100





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





      @property
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         torch.manual_seed(0		)
         lowerCamelCase__						=			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(UpperCAmelCase_		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         torch.manual_seed(0		)
         lowerCamelCase__						=			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(UpperCAmelCase_		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         torch.manual_seed(0		)

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

         lowerCamelCase__						=			UnCLIPTextProjModel(**UpperCAmelCase_		)
         return model





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         torch.manual_seed(0		)

         lowerCamelCase__						=			{
             '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',
         }

         lowerCamelCase__						=			UNetaDConditionModel(**UpperCAmelCase_		)
         return model





      @property
      def 					_lowerCamelCase  (  self		)   ->					str:
         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		)   ->					Optional[Any]:
         torch.manual_seed(0		)

         lowerCamelCase__						=			UNetaDModel(**self.dummy_super_res_kwargs		)
         return model





      @property
      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1		)

         lowerCamelCase__						=			UNetaDModel(**self.dummy_super_res_kwargs		)
         return model





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			self.dummy_decoder
         lowerCamelCase__						=			self.dummy_text_proj
         lowerCamelCase__						=			self.dummy_text_encoder
         lowerCamelCase__						=			self.dummy_tokenizer
         lowerCamelCase__						=			self.dummy_super_res_first
         lowerCamelCase__						=			self.dummy_super_res_last

         lowerCamelCase__						=			UnCLIPScheduler(
             variance_type="learned_range"    ,						prediction_type="epsilon"    ,						num_train_timesteps=1000    ,						)

         lowerCamelCase__						=			UnCLIPScheduler(
             variance_type="fixed_small_log"    ,						prediction_type="epsilon"    ,						num_train_timesteps=1000    ,						)

         lowerCamelCase__						=			CLIPImageProcessor(crop_size=32    ,						size=32		)

         lowerCamelCase__						=			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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=True		)   ->					List[Any]:
         lowerCamelCase__						=			floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(UpperCAmelCase_		)		).to(UpperCAmelCase_		)
         if str(UpperCAmelCase_		).startswith("mps"		):
            lowerCamelCase__						=			torch.manual_seed(UpperCAmelCase_		)
         else:
            lowerCamelCase__						=			torch.Generator(device=UpperCAmelCase_		).manual_seed(UpperCAmelCase_		)

         if pil_image:
            lowerCamelCase__						=			input_image * 0.5 + 0.5
            lowerCamelCase__						=			input_image.clamp(0    ,						1		)
            lowerCamelCase__						=			input_image.cpu().permute(0    ,						2    ,						3    ,						1		).float().numpy()
            lowerCamelCase__						=			DiffusionPipeline.numpy_to_pil(UpperCAmelCase_		)[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			'cpu'

         lowerCamelCase__						=			self.get_dummy_components()

         lowerCamelCase__						=			self.pipeline_class(**UpperCAmelCase_		)
         lowerCamelCase__						=			pipe.to(UpperCAmelCase_		)

         pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)

         lowerCamelCase__						=			pipe(**UpperCAmelCase_		)
         lowerCamelCase__						=			output.images

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)

         lowerCamelCase__						=			pipe(
             **UpperCAmelCase_    ,						return_dict=UpperCAmelCase_    ,						)[0]

         lowerCamelCase__						=			image[0, -3:, -3:, -1]
         lowerCamelCase__						=			image_from_tuple[0, -3:, -3:, -1]

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

         lowerCamelCase__						=			np.array(
             [
                 0.99_97,
                 0.00_02,
                 0.99_97,
                 0.99_97,
                 0.99_69,
                 0.00_23,
                 0.99_97,
                 0.99_69,
                 0.99_70,
             ]		)

         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		)   ->					List[Any]:
         lowerCamelCase__						=			'cpu'

         lowerCamelCase__						=			self.get_dummy_components()

         lowerCamelCase__						=			self.pipeline_class(**UpperCAmelCase_		)
         lowerCamelCase__						=			pipe.to(UpperCAmelCase_		)

         pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)

         lowerCamelCase__						=			pipe(**UpperCAmelCase_		)
         lowerCamelCase__						=			output.images

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)

         lowerCamelCase__						=			pipe(
             **UpperCAmelCase_    ,						return_dict=UpperCAmelCase_    ,						)[0]

         lowerCamelCase__						=			image[0, -3:, -3:, -1]
         lowerCamelCase__						=			image_from_tuple[0, -3:, -3:, -1]

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

         lowerCamelCase__						=			np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]		)

         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]:
         lowerCamelCase__						=			'cpu'

         lowerCamelCase__						=			self.get_dummy_components()

         lowerCamelCase__						=			self.pipeline_class(**UpperCAmelCase_		)
         lowerCamelCase__						=			pipe.to(UpperCAmelCase_		)

         pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)
         lowerCamelCase__						=			[
             pipeline_inputs['image'],
             pipeline_inputs['image'],
         ]

         lowerCamelCase__						=			pipe(**UpperCAmelCase_		)
         lowerCamelCase__						=			output.images

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)
         lowerCamelCase__						=			[
             tuple_pipeline_inputs['image'],
             tuple_pipeline_inputs['image'],
         ]

         lowerCamelCase__						=			pipe(
             **UpperCAmelCase_    ,						return_dict=UpperCAmelCase_    ,						)[0]

         lowerCamelCase__						=			image[0, -3:, -3:, -1]
         lowerCamelCase__						=			image_from_tuple[0, -3:, -3:, -1]

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

         lowerCamelCase__						=			np.array(
             [
                 0.99_97,
                 0.99_89,
                 0.00_08,
                 0.00_21,
                 0.99_60,
                 0.00_18,
                 0.00_14,
                 0.00_02,
                 0.99_33,
             ]		)

         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		)   ->					Union[str, Any]:
         lowerCamelCase__						=			torch.device("cpu"		)


         class 		_A       :
               __a								=	1






         lowerCamelCase__						=			self.get_dummy_components()

         lowerCamelCase__						=			self.pipeline_class(**UpperCAmelCase_		)
         lowerCamelCase__						=			pipe.to(UpperCAmelCase_		)

         pipe.set_progress_bar_config(disable=UpperCAmelCase_		)

         lowerCamelCase__						=			torch.Generator(device=UpperCAmelCase_		).manual_seed(0		)
         lowerCamelCase__						=			pipe.decoder.dtype
         lowerCamelCase__						=			1

         lowerCamelCase__						=			(
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         lowerCamelCase__						=			pipe.prepare_latents(
             UpperCAmelCase_    ,						dtype=UpperCAmelCase_    ,						device=UpperCAmelCase_    ,						generator=UpperCAmelCase_    ,						latents=UpperCAmelCase_    ,						scheduler=DummyScheduler()		)

         lowerCamelCase__						=			(
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         lowerCamelCase__						=			pipe.prepare_latents(
             UpperCAmelCase_    ,						dtype=UpperCAmelCase_    ,						device=UpperCAmelCase_    ,						generator=UpperCAmelCase_    ,						latents=UpperCAmelCase_    ,						scheduler=DummyScheduler()		)

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)

         lowerCamelCase__						=			pipe(
             **UpperCAmelCase_    ,						decoder_latents=UpperCAmelCase_    ,						super_res_latents=UpperCAmelCase_		).images

         lowerCamelCase__						=			self.get_dummy_inputs(UpperCAmelCase_    ,						pil_image=UpperCAmelCase_		)
         # Don't pass image, instead pass embedding
         lowerCamelCase__						=			pipeline_inputs.pop("image"		)
         lowerCamelCase__						=			pipe.image_encoder(UpperCAmelCase_		).image_embeds

         lowerCamelCase__						=			pipe(
             **UpperCAmelCase_    ,						decoder_latents=UpperCAmelCase_    ,						super_res_latents=UpperCAmelCase_    ,						image_embeddings=UpperCAmelCase_    ,						).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		)   ->					List[str]:
         lowerCamelCase__						=			torch_device == 'cpu'

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         lowerCamelCase__						=			1e-2

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





      @skip_mps
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			torch_device == 'cpu'
         lowerCamelCase__						=			True
         lowerCamelCase__						=			[
             'decoder_num_inference_steps',
             'super_res_num_inference_steps',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=UpperCAmelCase_    ,						relax_max_difference=UpperCAmelCase_    ,						additional_params_copy_to_batched_inputs=UpperCAmelCase_    ,						)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			[
             'decoder_num_inference_steps',
             'super_res_num_inference_steps',
         ]

         if torch_device == "mps":
            # TODO: MPS errors with larger batch sizes
            lowerCamelCase__						=			[2, 3]
            self._test_inference_batch_consistent(
                batch_sizes=UpperCAmelCase_    ,						additional_params_copy_to_batched_inputs=UpperCAmelCase_    ,						)
         else:
            self._test_inference_batch_consistent(
                additional_params_copy_to_batched_inputs=UpperCAmelCase_		)





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





      @skip_mps
      def 					_lowerCamelCase  (  self		)   ->					Any:
         return super().test_save_load_local()





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






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





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





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

         lowerCamelCase__						=			UnCLIPImageVariationPipeline.from_pretrained(
             "kakaobrain/karlo-v1-alpha-image-variations"    ,						torch_dtype=torch.floataa		)
         lowerCamelCase__						=			pipeline.to(UpperCAmelCase_		)
         pipeline.set_progress_bar_config(disable=UpperCAmelCase_		)

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)
         lowerCamelCase__						=			pipeline(
             UpperCAmelCase_    ,						generator=UpperCAmelCase_    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

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

         assert_mean_pixel_difference(UpperCAmelCase_    ,						UpperCAmelCase_    ,						15		)







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if length <= 0 or not isinstance(A__	,    A__   ):
      raise ValueError("Length must be a positive integer."   )
   return [n * (2 * n - 1) for n in range(A__   )]


if __name__ == "__main__":
     print(hexagonal_numbers(length=5))
     print(hexagonal_numbers(length=10))




