


'''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


lowerCAmelCase     :  Union[str, Any]											=    logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase     :  Optional[int]											=    """
    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					(_A ,    _A ,    _A=8       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							h // scale_factor**2
       if h % scale_factor**2 != 0:
              new_h += 1
       _lowerCAmelCase     :				List[str]       =							w // scale_factor**2
       if w % scale_factor**2 != 0:
              new_w += 1
       return new_h * scale_factor, new_w * scale_factor
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            super().__init__()

            self.register_modules(
                text_encoder=snake_case__   ,						tokenizer=snake_case__   ,						unet=snake_case__   ,						scheduler=snake_case__   ,						movq=snake_case__   ,						)
            _lowerCAmelCase     :				Union[str, Any]       =							2 ** (len(self.movq.config.block_out_channels ) - 1)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            if latents is None:
                   _lowerCAmelCase     :				Optional[Any]       =							randn_tensor(snake_case__   ,						generator=snake_case__   ,						device=snake_case__   ,						dtype=snake_case__ )
            else:
                   if latents.shape != shape:
                          raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
                   _lowerCAmelCase     :				Optional[Any]       =							latents.to(snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							latents * scheduler.init_noise_sigma
            return latents







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__=None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							len(snake_case__ ) if isinstance(snake_case__   ,						snake_case__ ) else 1
            # get prompt text embeddings
            _lowerCAmelCase     :				Optional[int]       =							self.tokenizer(
                snake_case__   ,						padding='max_length'   ,						truncation=snake_case__   ,						max_length=77   ,						return_attention_mask=snake_case__   ,						add_special_tokens=snake_case__   ,						return_tensors='pt'   ,						)

            _lowerCAmelCase     :				Dict       =							text_inputs.input_ids
            _lowerCAmelCase     :				Tuple       =							self.tokenizer(snake_case__   ,						padding='longest'   ,						return_tensors='pt' ).input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Optional[int]       =							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     :				str       =							text_input_ids.to(snake_case__ )
            _lowerCAmelCase     :				Any       =							text_inputs.attention_mask.to(snake_case__ )

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Dict       =							self.text_encoder(
                input_ids=snake_case__   ,						attention_mask=snake_case__ )

            _lowerCAmelCase     :				Optional[Any]       =							prompt_embeds.repeat_interleave(snake_case__   ,						dim=0 )
            _lowerCAmelCase     :				int       =							text_encoder_hidden_states.repeat_interleave(snake_case__   ,						dim=0 )
            _lowerCAmelCase     :				str       =							text_mask.repeat_interleave(snake_case__   ,						dim=0 )

            if do_classifier_free_guidance:
                   _lowerCAmelCase     :				List[str]
                   if negative_prompt is None:
                          _lowerCAmelCase     :				str       =							[''] * batch_size
                   elif type(snake_case__ ) is not type(snake_case__ ):
                          raise TypeError(
                              F'`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__ )} !='
                              F' {type(snake_case__ )}.' )
                   elif isinstance(snake_case__   ,						snake_case__ ):
                          _lowerCAmelCase     :				Any       =							[negative_prompt]
                   elif batch_size != len(snake_case__ ):
                          raise ValueError(
                              F'`negative_prompt`: {negative_prompt} has batch size {len(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     :				List[Any]       =							negative_prompt

                   _lowerCAmelCase     :				Union[str, Any]       =							self.tokenizer(
                       snake_case__   ,						padding='max_length'   ,						max_length=77   ,						truncation=snake_case__   ,						return_attention_mask=snake_case__   ,						add_special_tokens=snake_case__   ,						return_tensors='pt'   ,						)
                   _lowerCAmelCase     :				List[str]       =							uncond_input.input_ids.to(snake_case__ )
                   _lowerCAmelCase     :				Tuple       =							uncond_input.attention_mask.to(snake_case__ )

                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							self.text_encoder(
                       input_ids=snake_case__   ,						attention_mask=snake_case__ )

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

                   _lowerCAmelCase     :				List[str]       =							negative_prompt_embeds.shape[1]
                   _lowerCAmelCase     :				Any       =							negative_prompt_embeds.repeat(1   ,						snake_case__ )
                   _lowerCAmelCase     :				str       =							negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,						snake_case__ )

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

                   _lowerCAmelCase     :				Union[str, Any]       =							torch.cat([uncond_text_mask, text_mask] )

            return prompt_embeds, text_encoder_hidden_states, text_mask







     def a						(			self   ,						snake_case__=0 ):

            '''simple docstring'''

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

            _lowerCAmelCase     :				Optional[Any]       =							torch.device(F'cuda:{gpu_id}' )

            _lowerCAmelCase     :				List[Any]       =							[
                self.unet,
                self.text_encoder,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                          cpu_offload(snake_case__   ,						snake_case__ )







     def a						(			self   ,						snake_case__=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=snake_case__ )
                   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

            _lowerCAmelCase     :				Any       =							None
            for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							cpu_offload_with_hook(snake_case__   ,						snake_case__   ,						prev_module_hook=snake_case__ )

            if self.safety_checker is not None:
                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							cpu_offload_with_hook(self.safety_checker   ,						snake_case__   ,						prev_module_hook=snake_case__ )

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







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

            '''simple docstring'''

            if not hasattr(self.unet   ,						'_hf_hook' ):
                   return self.device
            for module in self.unet.modules():
                   if (
                       hasattr(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(snake_case__ )
     def __call__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = 512   ,						snake_case__ = 512   ,						snake_case__ = 100   ,						snake_case__ = 4.0   ,						snake_case__ = 1   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = "pil"   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            if isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							1
            elif isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Optional[int]       =							len(snake_case__ )
            else:
                   raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}' )

            _lowerCAmelCase     :				Union[str, Any]       =							self._execution_device

            _lowerCAmelCase     :				Dict       =							batch_size * num_images_per_prompt
            _lowerCAmelCase     :				Union[str, Any]       =							guidance_scale > 1.0

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							self._encode_prompt(
                snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ )

            if isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Optional[Any]       =							torch.cat(snake_case__   ,						dim=0 )
            if isinstance(snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				Union[str, Any]       =							torch.cat(snake_case__   ,						dim=0 )

            if do_classifier_free_guidance:
                   _lowerCAmelCase     :				Tuple       =							image_embeds.repeat_interleave(snake_case__   ,						dim=0 )
                   _lowerCAmelCase     :				Any       =							negative_image_embeds.repeat_interleave(snake_case__   ,						dim=0 )

            _lowerCAmelCase     :				Union[str, Any]       =							torch.cat([negative_image_embeds, image_embeds]   ,						dim=0 ).to(
                dtype=prompt_embeds.dtype   ,						device=snake_case__ )

            self.scheduler.set_timesteps(snake_case__   ,						device=snake_case__ )
            _lowerCAmelCase     :				int       =							self.scheduler.timesteps

            _lowerCAmelCase     :				str       =							self.unet.config.in_channels

            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							get_new_h_w(snake_case__   ,						snake_case__   ,						self.movq_scale_factor )

            # create initial latent
            _lowerCAmelCase     :				int       =							self.prepare_latents(
                (batch_size, num_channels_latents, height, width)   ,						text_encoder_hidden_states.dtype   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						self.scheduler   ,						)

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

                   _lowerCAmelCase     :				List[str]       =							{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
                   _lowerCAmelCase     :				Optional[int]       =							self.unet(
                       sample=snake_case__   ,						timestep=snake_case__   ,						encoder_hidden_states=snake_case__   ,						added_cond_kwargs=snake_case__   ,						return_dict=snake_case__   ,						)[0]

                   if do_classifier_free_guidance:
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							noise_pred.split(latents.shape[1]   ,						dim=1 )
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							noise_pred.chunk(2 )
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				Dict       =							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     :				str       =							noise_pred.split(latents.shape[1]   ,						dim=1 )

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

            if output_type == "pil":
                   _lowerCAmelCase     :				Union[str, Any]       =							self.numpy_to_pil(snake_case__ )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=snake_case__ )





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       if len(_A       ) <= 1:
              return [tuple(_A       )]

       _lowerCAmelCase     :				List[str]       =							[]

       def generate(_A ,    _A       ):
              _lowerCAmelCase     :				Any       =							[0] * n
              res.append(tuple(_A       )       )

              _lowerCAmelCase     :				Any       =							0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							arr[i], arr[0]
                            else:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							arr[i], arr[c[i]]
                            res.append(tuple(_A       )       )
                            c[i] += 1
                            _lowerCAmelCase     :				Optional[int]       =							0
                     else:
                            _lowerCAmelCase     :				Any       =							0
                            i += 1

       generate(len(_A       ) ,    _A       )
       return res


if __name__ == "__main__":
       lowerCAmelCase     :  int											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Optional[int]											=    [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))

