


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

_snake_case			 =	'''
    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 						lowercase_(    SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_=8      ):


  '''simple docstring'''

  lowerCamelCase					:   List[str]       =							h // scale_factor**2
  if h % scale_factor**2 != 0:
    new_h += 1
  lowerCamelCase					:   Any       =							w // scale_factor**2
  if w % scale_factor**2 != 0:
    new_w += 1
  return new_h * scale_factor, new_w * scale_factor







class       UpperCAmelCase_   (   __lowerCAmelCase       ):

   '''simple docstring'''




   def __init__(						self		,							__A		,							__A		,							__A		,							__A		,							__A		,							):






     """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		,							)
     lowerCamelCase					:   Optional[Any]       =							2 ** (len(self.movq.config.block_out_channels					) - 1)






   def _snake_case       (						self		,							__A		,							__A		,							__A		,							__A		,							__A		,							__A					):






     """simple docstring"""





     if latents is None:
       lowerCamelCase					:   Dict       =							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}"""					)
       lowerCamelCase					:   int       =							latents.to(_SCREAMING_SNAKE_CASE					)

     lowerCamelCase					:   Dict       =							latents * scheduler.init_noise_sigma
     return latents






   def _snake_case       (						self		,							__A		,							__A		,							__A		,							__A		,							__A=None		,							):






     """simple docstring"""





     lowerCamelCase					:   List[str]       =							len(_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE					) else 1
     # get prompt text embeddings
     lowerCamelCase					:   Optional[int]       =							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"		,							)

     lowerCamelCase					:   Any       =							text_inputs.input_ids
     lowerCamelCase					:   Union[str, Any]       =							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					):
       lowerCamelCase					:   List[Any]       =							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}"""					)

     lowerCamelCase					:   Optional[int]       =							text_input_ids.to(_SCREAMING_SNAKE_CASE					)
     lowerCamelCase					:   List[Any]       =							text_inputs.attention_mask.to(_SCREAMING_SNAKE_CASE					)

     lowerCamelCase       ,      lowerCamelCase					:   int       =							self.text_encoder(
         input_ids=_SCREAMING_SNAKE_CASE		,							attention_mask=_SCREAMING_SNAKE_CASE					)

     lowerCamelCase					:   List[str]       =							prompt_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,							dim=0					)
     lowerCamelCase					:   Union[str, Any]       =							text_encoder_hidden_states.repeat_interleave(_SCREAMING_SNAKE_CASE		,							dim=0					)
     lowerCamelCase					:   int       =							text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE		,							dim=0					)

     if do_classifier_free_guidance:
       lowerCamelCase					:   Union[str, Any]       =							42
       if negative_prompt is None:
         lowerCamelCase					:   List[str]       =							[""] * 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					):
         lowerCamelCase					:   int       =							[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:
         lowerCamelCase					:   Dict       =							negative_prompt

       lowerCamelCase					:   str       =							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"		,							)
       lowerCamelCase					:   str       =							uncond_input.input_ids.to(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase					:   List[Any]       =							uncond_input.attention_mask.to(_SCREAMING_SNAKE_CASE					)

       lowerCamelCase       ,      lowerCamelCase					:   int       =							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

       lowerCamelCase					:   Dict       =							negative_prompt_embeds.shape[1]
       lowerCamelCase					:   Any       =							negative_prompt_embeds.repeat(1		,							_SCREAMING_SNAKE_CASE					)
       lowerCamelCase					:   Any       =							negative_prompt_embeds.view(batch_size * num_images_per_prompt		,							_SCREAMING_SNAKE_CASE					)

       lowerCamelCase					:   Dict       =							uncond_text_encoder_hidden_states.shape[1]
       lowerCamelCase					:   Tuple       =							uncond_text_encoder_hidden_states.repeat(1		,							_SCREAMING_SNAKE_CASE		,							1					)
       lowerCamelCase					:   Optional[int]       =							uncond_text_encoder_hidden_states.view(
           batch_size * num_images_per_prompt		,							_SCREAMING_SNAKE_CASE		,							-1					)
       lowerCamelCase					:   str       =							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
       lowerCamelCase					:   int       =							torch.cat([negative_prompt_embeds, prompt_embeds]					)
       lowerCamelCase					:   Optional[Any]       =							torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

       lowerCamelCase					:   Optional[Any]       =							torch.cat([uncond_text_mask, text_mask]					)

     return prompt_embeds, text_encoder_hidden_states, text_mask






   def _snake_case       (						self		,							__A=0					):






     """simple docstring"""





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

     lowerCamelCase					:   Tuple       =							torch.device(F"""cuda:{gpu_id}"""					)

     lowerCamelCase					:   Optional[int]       =							[
         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 _snake_case       (						self		,							__A=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."					)

     lowerCamelCase					:   Optional[int]       =							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)

     lowerCamelCase					:   Dict       =							None
     for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
       lowerCamelCase       ,      lowerCamelCase					:   Any       =							cpu_offload_with_hook(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							prev_module_hook=_SCREAMING_SNAKE_CASE					)

     if self.safety_checker is not None:
       lowerCamelCase       ,      lowerCamelCase					:   str       =							cpu_offload_with_hook(self.safety_checker		,							_SCREAMING_SNAKE_CASE		,							prev_module_hook=_SCREAMING_SNAKE_CASE					)

     # We'll offload the last model manually.
     lowerCamelCase					:   List[Any]       =							hook






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






     """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		,							__A		,							__A		,							__A		,							__A = None		,							__A = 512		,							__A = 512		,							__A = 100		,							__A = 4.0		,							__A = 1		,							__A = None		,							__A = None		,							__A = "pil"		,							__A = True		,							):






     """simple docstring"""





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

     lowerCamelCase					:   Dict       =							self._execution_device

     lowerCamelCase					:   List[Any]       =							batch_size * num_images_per_prompt
     lowerCamelCase					:   Any       =							guidance_scale > 1.0

     lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase					:   Any       =							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					):
       lowerCamelCase					:   int       =							torch.cat(_SCREAMING_SNAKE_CASE		,							dim=0					)
     if isinstance(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE					):
       lowerCamelCase					:   Optional[int]       =							torch.cat(_SCREAMING_SNAKE_CASE		,							dim=0					)

     if do_classifier_free_guidance:
       lowerCamelCase					:   Optional[Any]       =							image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,							dim=0					)
       lowerCamelCase					:   Union[str, Any]       =							negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,							dim=0					)

     lowerCamelCase					:   List[Any]       =							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					)
     lowerCamelCase					:   List[str]       =							self.scheduler.timesteps

     lowerCamelCase					:   str       =							self.unet.config.in_channels

     lowerCamelCase       ,      lowerCamelCase					:   Tuple       =							get_new_h_w(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							self.movq_scale_factor					)

     # create initial latent
     lowerCamelCase					:   Union[str, Any]       =							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
       lowerCamelCase					:   Optional[int]       =							torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

       lowerCamelCase					:   int       =							{"text_embeds": prompt_embeds, "image_embeds": image_embeds}
       lowerCamelCase					:   List[str]       =							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:
         lowerCamelCase       ,      lowerCamelCase					:   Tuple       =							noise_pred.split(latents.shape[1]		,							dim=1					)
         lowerCamelCase       ,      lowerCamelCase					:   Tuple       =							noise_pred.chunk(2					)
         lowerCamelCase       ,      lowerCamelCase					:   Optional[Any]       =							variance_pred.chunk(2					)
         lowerCamelCase					:   Union[str, Any]       =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         lowerCamelCase					:   List[Any]       =							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"]
       ):
         lowerCamelCase       ,      lowerCamelCase					:   int       =							noise_pred.split(latents.shape[1]		,							dim=1					)

       # compute the previous noisy sample x_t -> x_t-1
       lowerCamelCase					:   Tuple       =							self.scheduler.step(
           _SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							generator=_SCREAMING_SNAKE_CASE		,							).prev_sample
     # post-processing
     lowerCamelCase					:   Dict       =							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"]:
       lowerCamelCase					:   Optional[int]       =							image * 0.5 + 0.5
       lowerCamelCase					:   int       =							image.clamp(0		,							1					)
       lowerCamelCase					:   Tuple       =							image.cpu().permute(0		,							2		,							3		,							1					).float().numpy()

     if output_type == "pil":
       lowerCamelCase					:   List[Any]       =							self.numpy_to_pil(_SCREAMING_SNAKE_CASE					)

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE					)









'''simple docstring'''





from math import ceil





def       __a      (  _UpperCamelCase:       int = 1_001       )		->					int:

     """simple docstring"""




     _snake_case				= 1

     for i in range(1   ,		int(ceil(n / 2.0       )       )       ):
          _snake_case				= 2 * i + 1
          _snake_case				= 2 * i
          _snake_case				= total + 4 * odd**2 - 6 * even

     return total


if __name__ == "__main__":
   import sys

   if len(sys.argv) == 1:
      print(solution())
   else:
      try:
         UpperCamelCase_		:     int          =		int(sys.argv[1])
         print(solution(n))
      except ValueError:
         print('''Invalid entry - please enter a number''')


