





'''simple docstring'''







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_   :				List[str]		=     logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case_   :				List[Any]		=     """
    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 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Dict,	SCREAMING_SNAKE_CASE__					:	Optional[int],	SCREAMING_SNAKE_CASE__					:	Optional[int]=8      )       ->		Optional[int]:
						UpperCAmelCase_	:					str     =     h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						UpperCAmelCase_	:					Tuple     =     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							:   Optional[Any]				,   __magic_name__							:   int				,   __magic_name__							:   Optional[int]				,   __magic_name__							:   Optional[int]				,   __magic_name__							:   str				,   __magic_name__							:   Optional[int]				,   )    ->				Tuple:







													"""simple docstring"""
													super().__init__()

													self.register_modules(
													    text_encoder=__lowerCAmelCase				,   tokenizer=__lowerCAmelCase				,   unet=__lowerCAmelCase				,   scheduler=__lowerCAmelCase				,   movq=__lowerCAmelCase				,   )
													UpperCAmelCase_	:					Any     =     2 ** (len(self.movq.config.block_out_channels      ) - 1)

							def 			UpperCAmelCase__		(							self							:   str				,   __magic_name__							:   Any				,   __magic_name__							:   Optional[Any]				,   __magic_name__							:   List[str]				,   __magic_name__							:   Optional[Any]				,   __magic_name__							:   List[Any]				,   __magic_name__							:   List[Any]      )    ->				str:







													"""simple docstring"""
													if latents is None:
																			UpperCAmelCase_	:					Tuple     =     randn_tensor(__lowerCAmelCase				,   generator=__lowerCAmelCase				,   device=__lowerCAmelCase				,   dtype=__lowerCAmelCase      )
													else:
																			if latents.shape != shape:
																									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
																			UpperCAmelCase_	:					Union[str, Any]     =     latents.to(__lowerCAmelCase      )

													UpperCAmelCase_	:					int     =     latents * scheduler.init_noise_sigma
													return latents

							def 			UpperCAmelCase__		(							self							:   List[str]				,   __magic_name__							:   List[str]				,   __magic_name__							:   List[str]				,   __magic_name__							:   List[str]				,   __magic_name__							:   str				,   __magic_name__							:   List[Any]=None				,   )    ->				Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					Union[str, Any]     =     len(__lowerCAmelCase      ) if isinstance(__lowerCAmelCase				,   __lowerCAmelCase      ) else 1
													# get prompt text embeddings
													UpperCAmelCase_	:					Optional[int]     =     self.tokenizer(
													    __lowerCAmelCase				,   padding='''max_length'''				,   truncation=__lowerCAmelCase				,   max_length=77				,   return_attention_mask=__lowerCAmelCase				,   add_special_tokens=__lowerCAmelCase				,   return_tensors='''pt'''				,   )

													UpperCAmelCase_	:					Optional[int]     =     text_inputs.input_ids
													UpperCAmelCase_	:					Optional[int]     =     self.tokenizer(__lowerCAmelCase				,   padding='''longest'''				,   return_tensors='''pt'''      ).input_ids

													if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__lowerCAmelCase				,   __lowerCAmelCase      ):
																			UpperCAmelCase_	:					str     =     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}"""      )

													UpperCAmelCase_	:					Tuple     =     text_input_ids.to(__lowerCAmelCase      )
													UpperCAmelCase_	:					Any     =     text_inputs.attention_mask.to(__lowerCAmelCase      )

													UpperCAmelCase_	:					Dict     =     self.text_encoder(
													    input_ids=__lowerCAmelCase				,   attention_mask=__lowerCAmelCase      )

													UpperCAmelCase_	:					int     =     prompt_embeds.repeat_interleave(__lowerCAmelCase				,   dim=0      )
													UpperCAmelCase_	:					Tuple     =     text_encoder_hidden_states.repeat_interleave(__lowerCAmelCase				,   dim=0      )
													UpperCAmelCase_	:					Union[str, Any]     =     text_mask.repeat_interleave(__lowerCAmelCase				,   dim=0      )

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

																			UpperCAmelCase_	:					Dict     =     self.tokenizer(
																			    __lowerCAmelCase				,   padding='''max_length'''				,   max_length=77				,   truncation=__lowerCAmelCase				,   return_attention_mask=__lowerCAmelCase				,   add_special_tokens=__lowerCAmelCase				,   return_tensors='''pt'''				,   )
																			UpperCAmelCase_	:					Optional[Any]     =     uncond_input.input_ids.to(__lowerCAmelCase      )
																			UpperCAmelCase_	:					Optional[int]     =     uncond_input.attention_mask.to(__lowerCAmelCase      )

																			UpperCAmelCase_	:					Dict     =     self.text_encoder(
																			    input_ids=__lowerCAmelCase				,   attention_mask=__lowerCAmelCase      )

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

																			UpperCAmelCase_	:					Tuple     =     negative_prompt_embeds.shape[1]
																			UpperCAmelCase_	:					int     =     negative_prompt_embeds.repeat(1				,   __lowerCAmelCase      )
																			UpperCAmelCase_	:					str     =     negative_prompt_embeds.view(batch_size * num_images_per_prompt				,   __lowerCAmelCase      )

																			UpperCAmelCase_	:					Any     =     uncond_text_encoder_hidden_states.shape[1]
																			UpperCAmelCase_	:					str     =     uncond_text_encoder_hidden_states.repeat(1				,   __lowerCAmelCase				,   1      )
																			UpperCAmelCase_	:					List[Any]     =     uncond_text_encoder_hidden_states.view(
																			    batch_size * num_images_per_prompt				,   __lowerCAmelCase				,   -1      )
																			UpperCAmelCase_	:					List[Any]     =     uncond_text_mask.repeat_interleave(__lowerCAmelCase				,   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
																			UpperCAmelCase_	:					Optional[int]     =     torch.cat([negative_prompt_embeds, prompt_embeds]      )
																			UpperCAmelCase_	:					Dict     =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

																			UpperCAmelCase_	:					str     =     torch.cat([uncond_text_mask, text_mask]      )

													return prompt_embeds, text_encoder_hidden_states, text_mask

							def 			UpperCAmelCase__		(							self							:   List[Any]				,   __magic_name__							:   Optional[Any]=0      )    ->				Tuple:







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

													UpperCAmelCase_	:					Dict     =     torch.device(F"""cuda:{gpu_id}"""      )

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

							def 			UpperCAmelCase__		(							self							:   str				,   __magic_name__							:   Tuple=0      )    ->				List[Any]:







													"""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.'''      )

													UpperCAmelCase_	:					Union[str, Any]     =     torch.device(F"""cuda:{gpu_id}"""      )

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

													UpperCAmelCase_	:					int     =     None
													for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
																			UpperCAmelCase_	:					Any     =     cpu_offload_with_hook(__lowerCAmelCase				,   __lowerCAmelCase				,   prev_module_hook=__lowerCAmelCase      )

													if self.safety_checker is not None:
																			UpperCAmelCase_	:					int     =     cpu_offload_with_hook(self.safety_checker				,   __lowerCAmelCase				,   prev_module_hook=__lowerCAmelCase      )

													# We'll offload the last model manually.
													UpperCAmelCase_	:					Tuple     =     hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 			UpperCAmelCase__		(							self							:   Any      )    ->				Tuple:







													"""simple docstring"""
													if not hasattr(self.unet				,   '''_hf_hook'''      ):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(__lowerCAmelCase				,   '''_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(__lowerCAmelCase      )
							def __call__(							self							:   Dict				,   __magic_name__							:   Union[str, Any]				,   __magic_name__							:   Tuple				,   __magic_name__							:   Union[str, Any]				,   __magic_name__							:   Any = None				,   __magic_name__							:   Dict = 5_12				,   __magic_name__							:   Dict = 5_12				,   __magic_name__							:   Optional[int] = 1_00				,   __magic_name__							:   int = 4.0				,   __magic_name__							:   Tuple = 1				,   __magic_name__							:   List[Any] = None				,   __magic_name__							:   Any = None				,   __magic_name__							:   str = "pil"				,   __magic_name__							:   str = True				,   )    ->				Union[str, Any]:







													"""simple docstring"""

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

													UpperCAmelCase_	:					List[Any]     =     self._execution_device

													UpperCAmelCase_	:					Optional[Any]     =     batch_size * num_images_per_prompt
													UpperCAmelCase_	:					Optional[Any]     =     guidance_scale > 1.0

													UpperCAmelCase_	:					List[Any]     =     self._encode_prompt(
													    __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase      )

													if isinstance(__lowerCAmelCase				,   __lowerCAmelCase      ):
																			UpperCAmelCase_	:					Union[str, Any]     =     torch.cat(__lowerCAmelCase				,   dim=0      )
													if isinstance(__lowerCAmelCase				,   __lowerCAmelCase      ):
																			UpperCAmelCase_	:					Any     =     torch.cat(__lowerCAmelCase				,   dim=0      )

													if do_classifier_free_guidance:
																			UpperCAmelCase_	:					Optional[Any]     =     image_embeds.repeat_interleave(__lowerCAmelCase				,   dim=0      )
																			UpperCAmelCase_	:					Tuple     =     negative_image_embeds.repeat_interleave(__lowerCAmelCase				,   dim=0      )

													UpperCAmelCase_	:					List[str]     =     torch.cat([negative_image_embeds, image_embeds]				,   dim=0      ).to(
													    dtype=prompt_embeds.dtype				,   device=__lowerCAmelCase      )

													self.scheduler.set_timesteps(__lowerCAmelCase				,   device=__lowerCAmelCase      )
													UpperCAmelCase_	:					str     =     self.scheduler.timesteps

													UpperCAmelCase_	:					Dict     =     self.unet.config.in_channels

													UpperCAmelCase_	:					int     =     get_new_h_w(__lowerCAmelCase				,   __lowerCAmelCase				,   self.movq_scale_factor      )

													# create initial latent
													UpperCAmelCase_	:					Optional[Any]     =     self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)				,   text_encoder_hidden_states.dtype				,   __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase				,   self.scheduler				,   )

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

																			UpperCAmelCase_	:					Any     =     {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
																			UpperCAmelCase_	:					Any     =     self.unet(
																			    sample=__lowerCAmelCase				,   timestep=__lowerCAmelCase				,   encoder_hidden_states=__lowerCAmelCase				,   added_cond_kwargs=__lowerCAmelCase				,   return_dict=__lowerCAmelCase				,   )[0]

																			if do_classifier_free_guidance:
																									UpperCAmelCase_	:					str     =     noise_pred.split(latents.shape[1]				,   dim=1      )
																									UpperCAmelCase_	:					Any     =     noise_pred.chunk(2      )
																									UpperCAmelCase_	:					Union[str, Any]     =     variance_pred.chunk(2      )
																									UpperCAmelCase_	:					List[Any]     =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									UpperCAmelCase_	:					Tuple     =     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"]
																			):
																									UpperCAmelCase_	:					Dict     =     noise_pred.split(latents.shape[1]				,   dim=1      )

																			# compute the previous noisy sample x_t -> x_t-1
																			UpperCAmelCase_	:					Tuple     =     self.scheduler.step(
																			    __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase				,   generator=__lowerCAmelCase				,   ).prev_sample
													# post-processing
													UpperCAmelCase_	:					Union[str, Any]     =     self.movq.decode(__lowerCAmelCase				,   force_not_quantize=__lowerCAmelCase      )['''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"]:
																			UpperCAmelCase_	:					Optional[Any]     =     image * 0.5 + 0.5
																			UpperCAmelCase_	:					str     =     image.clamp(0				,   1      )
																			UpperCAmelCase_	:					Optional[int]     =     image.cpu().permute(0				,   2				,   3				,   1      ).float().numpy()

													if output_type == "pil":
																			UpperCAmelCase_	:					Optional[Any]     =     self.numpy_to_pil(__lowerCAmelCase      )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__lowerCAmelCase      )






'''simple docstring'''

from __future__ import annotations

from random import random



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = None )  ->  str:
        lowercase__				:    Any  =					value
        lowercase__				:    Union[str, Any]  =					random()
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None

       def __repr__(						self )  ->  str:
        from pprint import pformat

        if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
        else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}	,   indent=1 )


       def __str__(						self )  ->  str:
        lowercase__				:    Any  =					str(self.value ) + ''' '''
        lowercase__				:    str  =					str(self.left or '''''' )
        lowercase__				:    List[str]  =					str(self.right or '''''' )
        return value + left + right


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if root is None:  # None tree is split into 2 Nones
  return None, None
 elif root.value is None:
  return None, None
 else:
  if value < root.value:
   lowercase__ ,							lowercase__				:    int  =					split(root.left		,     UpperCAmelCase     )
   return left, root
  else:
   lowercase__ ,							lowercase__				:    Optional[Any]  =					split(root.right		,     UpperCAmelCase     )
   return root, right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if (not left) or (not right):  # If one node is None, return the other
  return left or right
 elif left.prior < right.prior:
  lowercase__				:    Any  =					merge(left.right		,     UpperCAmelCase     )
  return left
 else:
  lowercase__				:    Dict  =					merge(UpperCAmelCase		,     right.left     )
  return right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					Node(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Tuple  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(merge(UpperCAmelCase		,     UpperCAmelCase     )		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Dict  =					split(UpperCAmelCase		,     value - 1     )
 lowercase__ ,							lowercase__				:    int  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not root:  # None
  return
 else:
  inorder(root.left     )
  print(root.value		,     end=''','''     )
  inorder(root.right     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 for arg in args.split():
  if arg[0] == "+":
   lowercase__				:    Tuple  =					insert(UpperCAmelCase		,     int(arg[1:]     )     )

  elif arg[0] == "-":
   lowercase__				:    Union[str, Any]  =					erase(UpperCAmelCase		,     int(arg[1:]     )     )

  else:
   print('''Unknown command'''     )

 return root
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					None
 print(
     '''enter numbers to create a tree, + value to add value into treap, '''
     '''- value to erase all nodes with value. \'q\' to quit. '''     )

 lowercase__				:    Dict  =					input()
 while args != "q":
  lowercase__				:    Any  =					interact_treap(UpperCAmelCase		,     UpperCAmelCase     )
  print(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					input()

 print('''good by!'''     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

