


'''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   __snake_case			(  _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   StableUnCLIPPipeline
    lowercase  					=   TEXT_TO_IMAGE_PARAMS
    lowercase  					=   TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase  					=   TEXT_TO_IMAGE_IMAGE_PARAMS
    lowercase  					=   TEXT_TO_IMAGE_IMAGE_PARAMS

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

    def __lowercase  (  self       :    Optional[Any]						)  ->    Dict:
      lowerCAmelCase_     : List[str]     =  32
      lowerCAmelCase_     : Any     =  embedder_hidden_size

      # prior components

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

      torch.manual_seed(0						)
      lowerCAmelCase_     : Any     =  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_     : Dict     =  PriorTransformer(
          num_attention_heads=2       ,		attention_head_dim=12       ,		embedding_dim=lowerCamelCase       ,		num_layers=1       ,		)

      torch.manual_seed(0						)
      lowerCAmelCase_     : Optional[int]     =  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_     : List[Any]     =  StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase						)
      lowerCAmelCase_     : Any     =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""						)

      torch.manual_seed(0						)
      lowerCAmelCase_     : Optional[int]     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

      torch.manual_seed(0						)
      lowerCAmelCase_     : Optional[int]     =  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_     : List[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=lowerCamelCase       ,		layers_per_block=1       ,		upcast_attention=lowerCamelCase       ,		use_linear_projection=lowerCamelCase       ,		)

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

      torch.manual_seed(0						)
      lowerCAmelCase_     : Tuple     =  AutoencoderKL()

      lowerCAmelCase_     : Dict     =  {
          # 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 __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Dict=0						)  ->    int:
      if str(lowerCamelCase						).startswith("""mps"""						):
        lowerCAmelCase_     : int     =  torch.manual_seed(lowerCamelCase						)
      else:
        lowerCAmelCase_     : Optional[int]     =  torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
      lowerCAmelCase_     : List[str]     =  {
          """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 __lowercase  (  self       :    List[Any]						)  ->    Dict:
      lowerCAmelCase_     : Optional[int]     =  torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase						)





    def __lowercase  (  self       :    Dict						)  ->    Dict:
      lowerCAmelCase_     : Optional[int]     =  torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase						)







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







    """simple docstring"""



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

    def __lowercase  (  self       :    Tuple						)  ->    Dict:
      lowerCAmelCase_     : Union[str, 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"""						)

      lowerCAmelCase_     : Dict     =  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_     : Dict     =  torch.Generator(device="""cpu"""						).manual_seed(0						)
      lowerCAmelCase_     : Union[str, Any]     =  pipe("""anime turle"""       ,		generator=lowerCamelCase       ,		output_type="""np"""						)

      lowerCAmelCase_     : Tuple     =  output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase       ,		lowerCamelCase						)





    def __lowercase  (  self       :    int						)  ->    List[Any]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      lowerCAmelCase_     : Dict     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""       ,		torch_dtype=torch.floataa						)
      lowerCAmelCase_     : Any     =  pipe.to(lowerCamelCase						)
      pipe.set_progress_bar_config(disable=lowerCamelCase						)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      lowerCAmelCase_     : str     =  pipe(
          """anime turtle"""       ,		prior_num_inference_steps=2       ,		num_inference_steps=2       ,		output_type="""np"""       ,		)

      lowerCAmelCase_     : Any     =  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







def 							UpperCamelCase_     (       A__   :  list				,       A__   :  int				,       A__   :  int				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







def 							UpperCamelCase_     (       A__   :  list   ):
  '''simple docstring'''





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



