





"""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       __a  (    _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		unittest.TestCase						):
     SCREAMING_SNAKE_CASE__					: Optional[Any]									=     StableUnCLIPPipeline
     SCREAMING_SNAKE_CASE__					: Dict									=     TEXT_TO_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE__					: str									=     TEXT_TO_IMAGE_BATCH_PARAMS
     SCREAMING_SNAKE_CASE__					: Tuple									=     TEXT_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE__					: List[Any]									=     TEXT_TO_IMAGE_IMAGE_PARAMS

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



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   32
            _lowerCamelCase             =   embedder_hidden_size

            # prior components

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

            torch.manual_seed(0  )
            _lowerCamelCase             =   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=10_00   ,	)  )

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

            torch.manual_seed(0  )
            _lowerCamelCase             =   DDPMScheduler(
                variance_type='fixed_small_log'   ,	prediction_type='sample'   ,	num_train_timesteps=10_00   ,	clip_sample=_lowerCAmelCase   ,	clip_sample_range=5.0   ,	beta_schedule='squaredcos_cap_v2'   ,	)

            # regular denoising components

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

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

            torch.manual_seed(0  )
            _lowerCamelCase             =   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=10_00   ,	)  )

            torch.manual_seed(0  )
            _lowerCamelCase             =   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  )
            _lowerCamelCase             =   DDIMScheduler(
                beta_schedule='scaled_linear'   ,	beta_start=0.00085   ,	beta_end=0.012   ,	prediction_type='v_prediction'   ,	set_alpha_to_one=_lowerCAmelCase   ,	steps_offset=1   ,	)

            torch.manual_seed(0  )
            _lowerCamelCase             =   AutoencoderKL()

            _lowerCamelCase             =   {
                # 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 		snake_case_     (			self   ,	a__   ,	a__=0  ):
            if str(_lowerCAmelCase  ).startswith('mps'  ):
                   _lowerCamelCase             =   torch.manual_seed(_lowerCAmelCase  )
            else:
                   _lowerCamelCase             =   torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
            _lowerCamelCase             =   {
                '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 		snake_case_     (			self  ):
            _lowerCamelCase             =   torch_device == 'cpu'

            self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   torch_device in ['cpu', 'mps']

            self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase  )







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



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



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'  )

            _lowerCamelCase             =   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()

            _lowerCamelCase             =   torch.Generator(device='cpu'  ).manual_seed(0  )
            _lowerCamelCase             =   pipe('anime turle'   ,	generator=_lowerCAmelCase   ,	output_type='np'  )

            _lowerCamelCase             =   output.images[0]

            assert image.shape == (7_68, 7_68, 3)

            assert_mean_pixel_difference(_lowerCAmelCase   ,	_lowerCAmelCase  )



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

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

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

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












def    UpperCAmelCase_				( __UpperCAmelCase		:      int				)   ->					int:
      assert isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				), f"The input value of [n={number}] is not an integer"

      if number == 1:
            return 2
      elif number < 1:
            SCREAMING_SNAKE_CASE_      		=     f"The input value of [n={number}] has to be > 0"
            raise ValueError(__UpperCAmelCase				)
      else:
            SCREAMING_SNAKE_CASE_      		=     sylvester(number - 1				)
            SCREAMING_SNAKE_CASE_      		=     num - 1
            SCREAMING_SNAKE_CASE_      		=     num
            return lower * upper + 1


if __name__ == "__main__":
   print(f'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')