





"""simple docstring"""





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__A											=						TypeVar("""KT""")
__A											=						TypeVar("""VT""")






class    _lowerCAmelCase  (	Generic[KT, VT]			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase = "root"       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				key
         lowerCAmelCase__     :List[Any]											=				value
         lowerCAmelCase__     :list[Node[KT, VT]]											=				[]






       def __repr__(       self ):



         '''simple docstring'''

         return F"Node({self.key}: {self.value})"






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return len(self.forward )






class    _lowerCAmelCase  (	Generic[KT, VT]			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase = 0.5       ,					__UpperCAmelCase = 1_6 ):



         '''simple docstring'''

         lowerCAmelCase__     :Node[KT, VT]											=				Node[KT, VT]()
         lowerCAmelCase__     :Optional[Any]											=				0
         lowerCAmelCase__     :Tuple											=				p
         lowerCAmelCase__     :List[Any]											=				max_level






       def __str__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				list(self )

         if len(__UpperCAmelCase ) == 0:
           return F"SkipList(level={self.level})"

         lowerCAmelCase__     :Union[str, Any]											=				max((len(str(__UpperCAmelCase ) ) for item in items)       ,					default=4 )
         lowerCAmelCase__     :Any											=				max(__UpperCAmelCase       ,					4 ) + 4

         lowerCAmelCase__     :Tuple											=				self.head
         lowerCAmelCase__     :Any											=				[]

         lowerCAmelCase__     :List[Any]											=				node.forward.copy()
         lines.append(F"[{node.key}]".ljust(__UpperCAmelCase       ,					'-' ) + '* ' * len(__UpperCAmelCase ) )
         lines.append(' ' * label_size + '| ' * len(__UpperCAmelCase ) )

         while len(node.forward ) != 0:
           lowerCAmelCase__     :Dict											=				node.forward[0]

           lines.append(
               F"[{node.key}]".ljust(__UpperCAmelCase       ,					'-' )
               + ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
           lines.append(' ' * label_size + '| ' * len(__UpperCAmelCase ) )
           lowerCAmelCase__     :Union[str, Any]											=				node.forward

         lines.append('None'.ljust(__UpperCAmelCase ) + '* ' * len(__UpperCAmelCase ) )
         return F"SkipList(level={self.level})\n" + "\n".join(__UpperCAmelCase )






       def __iter__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.head

         while len(node.forward ) != 0:
           yield node.forward[0].key
           lowerCAmelCase__     :str											=				node.forward[0]






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				1
         while random() < self.p and level < self.max_level:
           level += 1

         return level






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				[]

         lowerCAmelCase__     :Tuple											=				self.head

         for i in reversed(range(self.level ) ):
           # i < node.level - When node level is lesser than `i` decrement `i`.
           # node.forward[i].key < key - Jumping to node with key value higher
           #                             or equal to searched key would result
           #                             in skipping searched key.
           while i < node.level and node.forward[i].key < key:
             lowerCAmelCase__     :Any											=				node.forward[i]
           # Each leftmost node (relative to searched node) will potentially have to
           # be updated.
           update_vector.append(__UpperCAmelCase )

         update_vector.reverse()  # Note that we were inserting values in reverse order.

         # len(node.forward) != 0 - If current node doesn't contain any further
         #                          references then searched key is not present.
         # node.forward[0].key == key - Next node key should be equal to search key
         #                              if key is present.
         if len(node.forward ) != 0 and node.forward[0].key == key:
           return node.forward[0], update_vector
         else:
           return None, update_vector






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self._locate_node(__UpperCAmelCase )

         if node is not None:
           for i, update_node in enumerate(__UpperCAmelCase ):
             # Remove or replace all references to removed node.
             if update_node.level > i and update_node.forward[i].key == key:
               if node.level > i:
                 lowerCAmelCase__     :Optional[Any]											=				node.forward[i]
               else:
                 lowerCAmelCase__     :str											=				update_node.forward[:i]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				self._locate_node(__UpperCAmelCase )
         if node is not None:
           lowerCAmelCase__     :Tuple											=				value
         else:
           lowerCAmelCase__     :Optional[Any]											=				self.random_level()

           if level > self.level:
             # After level increase we have to add additional nodes to head.
             for _ in range(self.level - 1       ,					__UpperCAmelCase ):
               update_vector.append(self.head )
             lowerCAmelCase__     :int											=				level

           lowerCAmelCase__     :int											=				Node(__UpperCAmelCase       ,					__UpperCAmelCase )

           for i, update_node in enumerate(update_vector[:level] ):
             # Change references to pass through new node.
             if update_node.level > i:
               new_node.forward.append(update_node.forward[i] )

             if update_node.level < i + 1:
               update_node.forward.append(__UpperCAmelCase )
             else:
               lowerCAmelCase__     :Dict											=				new_node






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self._locate_node(__UpperCAmelCase )

         if node is not None:
           return node.value

         return None






def 					__A  ()   ->str:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				SkipList()
  skip_list.insert('Key1'	,  3     )
  skip_list.insert('Key2'	,  12     )
  skip_list.insert('Key3'	,  41     )
  skip_list.insert('Key4'	,  -19     )

  lowerCAmelCase__     :List[str]											=				skip_list.head
  lowerCAmelCase__     :Any											=				{}
  while node.level != 0:
    lowerCAmelCase__     :Optional[Any]											=				node.forward[0]
    lowerCAmelCase__     :Optional[int]											=				node.value

  assert len(_SCREAMING_SNAKE_CASE     ) == 4
  assert all_values["Key1"] == 3
  assert all_values["Key2"] == 12
  assert all_values["Key3"] == 41
  assert all_values["Key4"] == -19
def 					__A  ()   ->Dict:





  """simple docstring"""


  lowerCAmelCase__     :str											=				SkipList()
  skip_list.insert('Key1'	,  10     )
  skip_list.insert('Key1'	,  12     )

  skip_list.insert('Key5'	,  7     )
  skip_list.insert('Key7'	,  10     )
  skip_list.insert('Key10'	,  5     )

  skip_list.insert('Key7'	,  7     )
  skip_list.insert('Key5'	,  5     )
  skip_list.insert('Key10'	,  10     )

  lowerCAmelCase__     :str											=				skip_list.head
  lowerCAmelCase__     :List[Any]											=				{}
  while node.level != 0:
    lowerCAmelCase__     :int											=				node.forward[0]
    lowerCAmelCase__     :str											=				node.value

  if len(_SCREAMING_SNAKE_CASE     ) != 4:
    print()
  assert len(_SCREAMING_SNAKE_CASE     ) == 4
  assert all_values["Key1"] == 12
  assert all_values["Key7"] == 7
  assert all_values["Key5"] == 5
  assert all_values["Key10"] == 10
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				SkipList()
  assert skip_list.find('Some key'     ) is None
def 					__A  ()   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				SkipList()

  skip_list.insert('Key2'	,  20     )
  assert skip_list.find('Key2'     ) == 20

  skip_list.insert('Some Key'	,  10     )
  skip_list.insert('Key2'	,  8     )
  skip_list.insert('V'	,  13     )

  assert skip_list.find('Y'     ) is None
  assert skip_list.find('Key2'     ) == 8
  assert skip_list.find('Some Key'     ) == 10
  assert skip_list.find('V'     ) == 13
def 					__A  ()   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				SkipList()
  skip_list.delete('Some key'     )

  assert len(skip_list.head.forward     ) == 0
def 					__A  ()   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  14     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('V'     )
  skip_list.delete('Key2'     )

  assert skip_list.find('V'     ) is None
  assert skip_list.find('Key2'     ) is None
def 					__A  ()   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  14     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('V'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) == 14
  assert skip_list.find('Key1'     ) == 12
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('X'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) == 12
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('Key1'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) is None
  assert skip_list.find('Key2'     ) == 15

  skip_list.delete('Key2'     )
  assert skip_list.find('V'     ) is None
  assert skip_list.find('X'     ) is None
  assert skip_list.find('Key1'     ) is None
  assert skip_list.find('Key2'     ) is None
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :str											=				SkipList()

  skip_list.insert('Key1'	,  12     )
  skip_list.insert('V'	,  13     )
  skip_list.insert('X'	,  142     )
  skip_list.insert('Key2'	,  15     )

  skip_list.delete('X'     )

  def traverse_keys(_SCREAMING_SNAKE_CASE     ):
    yield node.key
    for forward_node in node.forward:
      yield from traverse_keys(_SCREAMING_SNAKE_CASE     )

  assert len(set(traverse_keys(skip_list.head     )     )     ) == 4
def 					__A  ()   ->Optional[int]:





  """simple docstring"""


  def is_sorted(_SCREAMING_SNAKE_CASE     ):
    return all(next_item >= item for item, next_item in zip(_SCREAMING_SNAKE_CASE	,  lst[1:]     )     )

  lowerCAmelCase__     :Optional[Any]											=				SkipList()
  for i in range(10     ):
    skip_list.insert(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
  skip_list.delete(5     )
  skip_list.delete(8     )
  skip_list.delete(2     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
  skip_list.insert(-12	,  -12     )
  skip_list.insert(77	,  77     )
  assert is_sorted(list(_SCREAMING_SNAKE_CASE     )     )
def 					__A  ()   ->Any:





  """simple docstring"""


  for _ in range(100     ):
    # Repeat test 100 times due to the probabilistic nature of skip list
    # random values == random bugs
    test_insert()
    test_insert_overrides_existing_value()

    test_searching_empty_list_returns_none()
    test_search()

    test_deleting_item_from_empty_list_do_nothing()
    test_deleted_items_are_not_founded_by_find_method()
    test_delete_removes_only_given_key()
    test_delete_doesnt_leave_dead_nodes()

    test_iter_always_yields_sorted_values()
def 					__A  ()   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				SkipList()
  skip_list.insert(2	,  '2'     )
  skip_list.insert(4	,  '4'     )
  skip_list.insert(6	,  '4'     )
  skip_list.insert(4	,  '5'     )
  skip_list.insert(8	,  '4'     )
  skip_list.insert(9	,  '4'     )

  skip_list.delete(4     )

  print(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


"""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       lowerCAmelCase_		(				A__				,				A__				,				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		StableUnCLIPPipeline
							_snake_case						      =		TEXT_TO_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS

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




							def    A__						( self				)       ->  Optional[Any]:
													__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=snake_case_    ,				projection_dim=snake_case_    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

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

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDPMScheduler(
													    variance_type="""fixed_small_log"""    ,				prediction_type="""sample"""    ,				num_train_timesteps=1_000    ,				clip_sample=snake_case_    ,				clip_sample_range=5.0    ,				beta_schedule="""squaredcos_cap_v2"""    ,				)

													# regular denoising components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  StableUnCLIPImageNormalizer(embedding_dim=snake_case_				)
													__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=snake_case_    ,				projection_dim=32    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													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=snake_case_    ,				layers_per_block=1    ,				upcast_attention=snake_case_    ,				use_linear_projection=snake_case_    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDIMScheduler(
													    beta_schedule="""scaled_linear"""    ,				beta_start=0.00_085    ,				beta_end=0.012    ,				prediction_type="""v_prediction"""    ,				set_alpha_to_one=snake_case_    ,				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    A__						( self    ,				snake_case_    ,				snake_case_=0				)       ->  Optional[Any]:
													if str(snake_case_				).startswith("""mps"""				):
																			__lowerCAmelCase     =  torch.manual_seed(snake_case_				)
													else:
																			__lowerCAmelCase     =  torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
													__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    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device == """cpu"""

													self._test_attention_slicing_forward_pass(test_max_difference=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device in ["""cpu""", """mps"""]

													self._test_inference_batch_single_identical(test_max_difference=snake_case_				)
@slow
@require_torch_gpu
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





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




							def    A__						( self				)       ->  Tuple:
													__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(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													# 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=snake_case_    ,				output_type="""np"""				)

													__lowerCAmelCase     =  output.images[0]

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

													assert_mean_pixel_difference(snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Tuple:
													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(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													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
