



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


__snake_case		:int   	=  logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case		:Tuple   	=  '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=8       ):
    __a		 =       h // scale_factor**2
    if h % scale_factor**2 != 0:
        new_h += 1
    __a		 =       w // scale_factor**2
    if w % scale_factor**2 != 0:
        new_w += 1
    return new_h * scale_factor, new_w * scale_factor

class 					_A (			A_					):



       def __init__(	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  int  ,	):


           '''simple docstring'''

           super().__init__()

           self.register_modules(
               text_encoder=__SCREAMING_SNAKE_CASE  ,	tokenizer=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE  ,	movq=__SCREAMING_SNAKE_CASE  ,	)
           __a		 =       2 ** (len(self.movq.config.block_out_channels) - 1)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           if latents is None:
               __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=__SCREAMING_SNAKE_CASE  ,	dtype=__SCREAMING_SNAKE_CASE)
           else:
               if latents.shape != shape:
                   raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
               __a		 =       latents.to(__SCREAMING_SNAKE_CASE)

           __a		 =       latents * scheduler.init_noise_sigma
           return latents



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	):


           '''simple docstring'''

           __a		 =       len(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else 1
           # get prompt text embeddings
           __a		 =       self.tokenizer(
               __SCREAMING_SNAKE_CASE  ,	padding='''max_length'''  ,	truncation=__SCREAMING_SNAKE_CASE  ,	max_length=77  ,	return_attention_mask=__SCREAMING_SNAKE_CASE  ,	add_special_tokens=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt'''  ,	)

           __a		 =       text_inputs.input_ids
           __a		 =       self.tokenizer(__SCREAMING_SNAKE_CASE  ,	padding='''longest'''  ,	return_tensors='''pt''').input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   F' {self.tokenizer.model_max_length} tokens: {removed_text}')

           __a		 =       text_input_ids.to(__SCREAMING_SNAKE_CASE)
           __a		 =       text_inputs.attention_mask.to(__SCREAMING_SNAKE_CASE)

           __a		 =       self.text_encoder(
               input_ids=__SCREAMING_SNAKE_CASE  ,	attention_mask=__SCREAMING_SNAKE_CASE)

           __a		 =       prompt_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)
           __a		 =       text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)
           __a		 =       text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)

           if do_classifier_free_guidance:
               __a		 =       42
               if negative_prompt is None:
                   __a		 =       [""""""] * batch_size
               elif type(__SCREAMING_SNAKE_CASE) is not type(__SCREAMING_SNAKE_CASE):
                   raise TypeError(
                       F'`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE)} !='
                       F' {type(__SCREAMING_SNAKE_CASE)}.')
               elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
                   __a		 =       [negative_prompt]
               elif batch_size != len(__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE)}, but `prompt`:'
                       F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                       ''' the batch size of `prompt`.''')
               else:
                   __a		 =       negative_prompt

               __a		 =       self.tokenizer(
                   __SCREAMING_SNAKE_CASE  ,	padding='''max_length'''  ,	max_length=77  ,	truncation=__SCREAMING_SNAKE_CASE  ,	return_attention_mask=__SCREAMING_SNAKE_CASE  ,	add_special_tokens=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt'''  ,	)
               __a		 =       uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE)
               __a		 =       uncond_input.attention_mask.to(__SCREAMING_SNAKE_CASE)

               __a		 =       self.text_encoder(
                   input_ids=__SCREAMING_SNAKE_CASE  ,	attention_mask=__SCREAMING_SNAKE_CASE)

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

               __a		 =       negative_prompt_embeds.shape[1]
               __a		 =       negative_prompt_embeds.repeat(1  ,	__SCREAMING_SNAKE_CASE)
               __a		 =       negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,	__SCREAMING_SNAKE_CASE)

               __a		 =       uncond_text_encoder_hidden_states.shape[1]
               __a		 =       uncond_text_encoder_hidden_states.repeat(1  ,	__SCREAMING_SNAKE_CASE  ,	1)
               __a		 =       uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt  ,	__SCREAMING_SNAKE_CASE  ,	-1)
               __a		 =       uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)

               # done duplicates

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               __a		 =       torch.cat([negative_prompt_embeds, prompt_embeds])
               __a		 =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

               __a		 =       torch.cat([uncond_text_mask, text_mask])

           return prompt_embeds, text_encoder_hidden_states, text_mask



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Dict=0):


           '''simple docstring'''

           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError('''Please install accelerate via `pip install accelerate`''')

           __a		 =       torch.device(F'cuda:{gpu_id}')

           __a		 =       [
               self.unet,
               self.text_encoder,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0):


           '''simple docstring'''

           if is_accelerate_available() and is_accelerate_version('''>='''  ,	'''0.17.0.dev0'''):
               from accelerate import cpu_offload_with_hook
           else:
               raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''')

           __a		 =       torch.device(F'cuda:{gpu_id}')

           if self.device.type != "cpu":
               self.to('''cpu'''  ,	silence_dtype_warnings=__SCREAMING_SNAKE_CASE)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           __a		 =       None
           for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
               __a		 =       cpu_offload_with_hook(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	prev_module_hook=__SCREAMING_SNAKE_CASE)

           if self.safety_checker is not None:
               __a		 =       cpu_offload_with_hook(self.safety_checker  ,	__SCREAMING_SNAKE_CASE  ,	prev_module_hook=__SCREAMING_SNAKE_CASE)

           # We'll offload the last model manually.
           __a		 =       hook



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           if not hasattr(self.unet  ,	'''_hf_hook'''):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(__SCREAMING_SNAKE_CASE  ,	'''_hf_hook''')
                   and hasattr(module._hf_hook  ,	'''execution_device''')
                   and module._hf_hook.execution_device is not None
               ):
                   return torch.device(module._hf_hook.execution_device)
           return self.device





       @torch.no_grad()
       @replace_example_docstring(__SCREAMING_SNAKE_CASE)
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = 512  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = 512  ,	__SCREAMING_SNAKE_CASE							:  int = 100  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = 4.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = None  ,	__SCREAMING_SNAKE_CASE							:  List[Any] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  List[str] = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       len(__SCREAMING_SNAKE_CASE)
           else:
               raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}')

           __a		 =       self._execution_device

           __a		 =       batch_size * num_images_per_prompt
           __a		 =       guidance_scale > 1.0

           __a		 =       self._encode_prompt(
               __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       torch.cat(__SCREAMING_SNAKE_CASE  ,	dim=0)
           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       torch.cat(__SCREAMING_SNAKE_CASE  ,	dim=0)

           if do_classifier_free_guidance:
               __a		 =       image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)
               __a		 =       negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE  ,	dim=0)

           __a		 =       torch.cat([negative_image_embeds, image_embeds]  ,	dim=0).to(
               dtype=prompt_embeds.dtype  ,	device=__SCREAMING_SNAKE_CASE)

           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=__SCREAMING_SNAKE_CASE)
           __a		 =       self.scheduler.timesteps

           __a		 =       self.unet.config.in_channels

           __a		 =       get_new_h_w(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	self.movq_scale_factor)

           # create initial latent
           __a		 =       self.prepare_latents(
               (batch_size, num_channels_latents, height, width)  ,	text_encoder_hidden_states.dtype  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	self.scheduler  ,	)

           for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE)):
               # expand the latents if we are doing classifier free guidance
               __a		 =       torch.cat([latents] * 2) if do_classifier_free_guidance else latents

               __a		 =       {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
               __a		 =       self.unet(
                   sample=__SCREAMING_SNAKE_CASE  ,	timestep=__SCREAMING_SNAKE_CASE  ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE  ,	added_cond_kwargs=__SCREAMING_SNAKE_CASE  ,	return_dict=__SCREAMING_SNAKE_CASE  ,	)[0]

               if do_classifier_free_guidance:
                   __a		 =       noise_pred.split(latents.shape[1]  ,	dim=1)
                   __a		 =       noise_pred.chunk(2)
                   __a		 =       variance_pred.chunk(2)
                   __a		 =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   __a		 =       torch.cat([noise_pred, variance_pred_text]  ,	dim=1)

               if not (
                   hasattr(self.scheduler.config  ,	'''variance_type''')
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                   __a		 =       noise_pred.split(latents.shape[1]  ,	dim=1)

               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(
                   __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	).prev_sample
           # post-processing
           __a		 =       self.movq.decode(__SCREAMING_SNAKE_CASE  ,	force_not_quantize=__SCREAMING_SNAKE_CASE)["""sample"""]

           if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}')

           if output_type in ["np", "pil"]:
               __a		 =       image * 0.5 + 0.5
               __a		 =       image.clamp(0  ,	1)
               __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).float().numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)








from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__snake_case		:Any   	=  TypeVar('''KT''')
__snake_case		:List[str]   	=  TypeVar('''VT''')

class 					_A (			Generic[KT, VT]					):



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  KT | str = "root"  ,	__SCREAMING_SNAKE_CASE							:  VT | None = None):


           '''simple docstring'''

           __a		 =       key
           __a		 =       value
           __a		 =       []



       def __repr__(	self							:  Dict):


           '''simple docstring'''

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





       @property
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           return len(self.forward)


class 					_A (			Generic[KT, VT]					):



       def __init__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  float = 0.5  ,	__SCREAMING_SNAKE_CASE							:  int = 16):


           '''simple docstring'''

           __a		 =       Node[KT, VT]()
           __a		 =       0
           __a		 =       p
           __a		 =       max_level



       def __str__(	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       list(self)

           if len(__SCREAMING_SNAKE_CASE) == 0:
               return F'SkipList(level={self.level})'

           __a		 =       max((len(str(__SCREAMING_SNAKE_CASE)) for item in items)  ,	default=4)
           __a		 =       max(__SCREAMING_SNAKE_CASE  ,	4) + 4

           __a		 =       self.head
           __a		 =       []

           __a		 =       node.forward.copy()
           lines.append(F'[{node.key}]'.ljust(__SCREAMING_SNAKE_CASE  ,	'''-''') + '''* ''' * len(__SCREAMING_SNAKE_CASE))
           lines.append(''' ''' * label_size + '''| ''' * len(__SCREAMING_SNAKE_CASE))

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

               lines.append(
                   F'[{node.key}]'.ljust(__SCREAMING_SNAKE_CASE  ,	'''-''')
                   + ''' '''.join(str(n.key) if n.key == node.key else '''|''' for n in forwards))
               lines.append(''' ''' * label_size + '''| ''' * len(__SCREAMING_SNAKE_CASE))
               __a		 =       node.forward

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



       def __iter__(	self							:  int):


           '''simple docstring'''

           __a		 =       self.head

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



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

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

           return level



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       []

           __a		 =       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:
                   __a		 =       node.forward[i]
               # Each leftmost node (relative to searched node) will potentially have to
               # be updated.
               update_vector.append(__SCREAMING_SNAKE_CASE)

           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 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  KT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)

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



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  KT  ,	__SCREAMING_SNAKE_CASE							:  VT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)
           if node is not None:
               __a		 =       value
           else:
               __a		 =       self.random_level()

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

               __a		 =       Node(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

               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(__SCREAMING_SNAKE_CASE)
                   else:
                       __a		 =       new_node





       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  VT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)

           if node is not None:
               return node.value

           return None





def   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.insert('''Key1'''     ,				3       )
    skip_list.insert('''Key2'''     ,				12       )
    skip_list.insert('''Key3'''     ,				41       )
    skip_list.insert('''Key4'''     ,				-19       )

    __a		 =       skip_list.head
    __a		 =       {}
    while node.level != 0:
        __a		 =       node.forward[0]
        __a		 =       node.value

    assert len(_UpperCAmelCase       ) == 4
    assert all_values["Key1"] == 3
    assert all_values["Key2"] == 12
    assert all_values["Key3"] == 41
    assert all_values["Key4"] == -19





def   __snake_case			(    ):
    __a		 =       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       )

    __a		 =       skip_list.head
    __a		 =       {}
    while node.level != 0:
        __a		 =       node.forward[0]
        __a		 =       node.value

    if len(_UpperCAmelCase       ) != 4:
        print()
    assert len(_UpperCAmelCase       ) == 4
    assert all_values["Key1"] == 12
    assert all_values["Key7"] == 7
    assert all_values["Key5"] == 5
    assert all_values["Key10"] == 10





def   __snake_case			(    ):
    __a		 =       SkipList()
    assert skip_list.find('''Some key'''       ) is None





def   __snake_case			(    ):
    __a		 =       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   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.delete('''Some key'''       )

    assert len(skip_list.head.forward       ) == 0





def   __snake_case			(    ):
    __a		 =       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   __snake_case			(    ):
    __a		 =       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   __snake_case			(    ):
    __a		 =       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(_UpperCAmelCase       ):
        yield node.key
        for forward_node in node.forward:
            yield from traverse_keys(_UpperCAmelCase       )

    assert len(set(traverse_keys(skip_list.head       )       )       ) == 4





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

    __a		 =       SkipList()
    for i in range(10       ):
        skip_list.insert(_UpperCAmelCase     ,				_UpperCAmelCase       )
    assert is_sorted(list(_UpperCAmelCase       )       )
    skip_list.delete(5       )
    skip_list.delete(8       )
    skip_list.delete(2       )
    assert is_sorted(list(_UpperCAmelCase       )       )
    skip_list.insert(-12     ,				-12       )
    skip_list.insert(77     ,				77       )
    assert is_sorted(list(_UpperCAmelCase       )       )





def   __snake_case			(    ):
    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   __snake_case			(    ):
    __a		 =       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(_UpperCAmelCase       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





