


"""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  (  _a   ,_a   ,_a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Dict     =	StableUnCLIPPipeline
    UpperCAmelCase	:							Any     =	TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase	:							Any     =	TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase	:							Union[str, Any]     =	TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase	:							Tuple     =	TEXT_TO_IMAGE_IMAGE_PARAMS

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






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      32
      a						:		Dict											=      embedder_hidden_size

      # prior components

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

      torch.manual_seed(0)
      a						:		Optional[int]											=      CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=__UpperCAmelCase    ,			projection_dim=__UpperCAmelCase    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1000    ,			))

      torch.manual_seed(0)
      a						:		Optional[int]											=      PriorTransformer(
          num_attention_heads=2    ,			attention_head_dim=12    ,			embedding_dim=__UpperCAmelCase    ,			num_layers=1    ,			)

      torch.manual_seed(0)
      a						:		str											=      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						:		Tuple											=      StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase)
      a						:		Dict											=      DDPMScheduler(beta_schedule="squaredcos_cap_v2")

      torch.manual_seed(0)
      a						:		Union[str, Any]											=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      torch.manual_seed(0)
      a						:		Union[str, Any]											=      CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=__UpperCAmelCase    ,			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=1000    ,			))

      torch.manual_seed(0)
      a						:		Any											=      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[str]											=      DDIMScheduler(
          beta_schedule="scaled_linear"    ,			beta_start=0.00_085    ,			beta_end=0.012    ,			prediction_type="v_prediction"    ,			set_alpha_to_one=__UpperCAmelCase    ,			steps_offset=1    ,			)

      torch.manual_seed(0)
      a						:		Any											=      AutoencoderKL()

      a						:		Optional[int]											=      {
          # 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    :   Dict    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   str=0):
      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Optional[Any]											=      torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		str											=      torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		int											=      {
          "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    :   Dict):
      a						:		Any											=      torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase)






    def      __snake_case							( self    :   int):
      a						:		Any											=      torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase)






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







    """simple docstring"""




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






    def      __snake_case							( self    :   List[Any]):
      a						:		List[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						:		List[Any]											=      torch.Generator(device="cpu").manual_seed(0)
      a						:		str											=      pipe("anime turle"    ,			generator=__UpperCAmelCase    ,			output_type="np")

      a						:		Optional[Any]											=      output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Any):
      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						:		Any											=      pipe(
          "anime turtle"    ,			prior_num_inference_steps=2    ,			num_inference_steps=2    ,			output_type="np"    ,			)

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










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






