
"""simple docstring"""




from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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

class 		a__						(      a_,			a_						):
   @register_to_config
   def __init__(			self	,    _a	,    _a = None	,    _a = None				):
          super().__init__()

          lowercase    :				Optional[Any]       		=							learnable

          if self.learnable:
                 assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                 assert length is not None, "learnable=True requires `length` to be set"

                 lowercase    :				List[str]       		=							torch.zeros(_a	,    _a				)
          else:
                 lowercase    :				Optional[int]       		=							None

          lowercase    :				str       		=							torch.nn.Parameter(_a				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   def __init__(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    ):
          super().__init__()

          self.register_modules(
              vqvae=_a	,    transformer=_a	,    text_encoder=_a	,    tokenizer=_a	,    scheduler=_a	,    learned_classifier_free_sampling_embeddings=_a	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Union[str, Any]       		=							len(_a				) if isinstance(_a	,    _a				) else 1

          # get prompt text embeddings
          lowercase    :				int       		=							self.tokenizer(
              _a	,    padding="max_length"	,    max_length=self.tokenizer.model_max_length	,    return_tensors="pt"	,    )
          lowercase    :				Dict       		=							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 lowercase    :				List[Any]       		=							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
                 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}"""				)
                 lowercase    :				int       		=							text_input_ids[:, : self.tokenizer.model_max_length]
          lowercase    :				Optional[int]       		=							self.text_encoder(text_input_ids.to(self.device				)				)[0]

          # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
          # While CLIP does normalize the pooled output of the text transformer when combining
          # the image and text embeddings, CLIP does not directly normalize the last hidden state.
          #
          # CLIP normalizing the pooled output.
          # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
          lowercase    :				Optional[int]       		=							prompt_embeds / prompt_embeds.norm(dim=-1	,    keepdim=_a				)

          # duplicate text embeddings for each generation per prompt
          lowercase    :				Dict       		=							prompt_embeds.repeat_interleave(_a	,    dim=0				)

          if do_classifier_free_guidance:
                 if self.learned_classifier_free_sampling_embeddings.learnable:
                        lowercase    :				str       		=							self.learned_classifier_free_sampling_embeddings.embeddings
                        lowercase    :				str       		=							negative_prompt_embeds.unsqueeze(0				).repeat(_a	,    1	,    1				)
                 else:
                        lowercase    :				List[str]       		=							[""] * batch_size

                        lowercase    :				Tuple       		=							text_input_ids.shape[-1]
                        lowercase    :				Tuple       		=							self.tokenizer(
                            _a	,    padding="max_length"	,    max_length=_a	,    truncation=_a	,    return_tensors="pt"	,    )
                        lowercase    :				List[str]       		=							self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
                        # See comment for normalizing text embeddings
                        lowercase    :				Union[str, Any]       		=							negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1	,    keepdim=_a				)

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 lowercase    :				Tuple       		=							negative_prompt_embeds.shape[1]
                 lowercase    :				Optional[int]       		=							negative_prompt_embeds.repeat(1	,    _a	,    1				)
                 lowercase    :				Optional[Any]       		=							negative_prompt_embeds.view(batch_size * num_images_per_prompt	,    _a	,    -1				)

                 # 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
                 lowercase    :				List[Any]       		=							torch.cat([negative_prompt_embeds, prompt_embeds]				)

          return prompt_embeds
   @torch.no_grad()
   def __call__(			self	,    _a	,    _a = 100	,    _a = 5.0	,    _a = 1.0	,    _a = 1	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    ):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[str]       		=							1
          elif isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							len(_a				)
          else:
                 raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_a				)}"""				)

          lowercase    :				Any       		=							batch_size * num_images_per_prompt

          lowercase    :				List[str]       		=							guidance_scale > 1.0

          lowercase    :				Tuple       		=							self._encode_prompt(_a	,    _a	,    _a				)

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(_a	,    _a				) or callback_steps <= 0)
          ):
                 raise ValueError(
                     f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                     f""" {type(_a				)}."""				)

          # get the initial completely masked latents unless the user supplied it

          lowercase    :				Optional[Any]       		=							(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
                 lowercase    :				Optional[Any]       		=							self.transformer.num_vector_embeds - 1
                 lowercase    :				Optional[Any]       		=							torch.full(_a	,    _a				).to(self.device				)
          else:
                 if latents.shape != latents_shape:
                        raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
                 if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                        raise ValueError(
                            "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
                            f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""				)
                 lowercase    :				List[str]       		=							latents.to(self.device				)

          # set timesteps
          self.scheduler.set_timesteps(_a	,    device=self.device				)

          lowercase    :				List[str]       		=							self.scheduler.timesteps.to(self.device				)

          lowercase    :				Optional[int]       		=							latents

          for i, t in enumerate(self.progress_bar(_a				)				):
                 # expand the sample if we are doing classifier free guidance
                 lowercase    :				Union[str, Any]       		=							torch.cat([sample] * 2				) if do_classifier_free_guidance else sample

                 # predict the un-noised image
                 # model_output == `log_p_x_0`
                 lowercase    :				List[Any]       		=							self.transformer(_a	,    encoder_hidden_states=_a	,    timestep=_a				).sample

                 if do_classifier_free_guidance:
                        lowercase		,    lowercase    :				Optional[int]       		=							model_output.chunk(2				)
                        lowercase    :				str       		=							model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                        model_output -= torch.logsumexp(_a	,    dim=1	,    keepdim=_a				)

                 lowercase    :				Optional[int]       		=							self.truncate(_a	,    _a				)

                 # remove `log(0)`'s (`-inf`s)
                 lowercase    :				Union[str, Any]       		=							model_output.clamp(-70				)

                 # compute the previous noisy sample x_t -> x_t-1
                 lowercase    :				int       		=							self.scheduler.step(_a	,    timestep=_a	,    sample=_a	,    generator=_a				).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                        callback(_a	,    _a	,    _a				)

          lowercase    :				Dict       		=							self.vqvae.config.vq_embed_dim
          lowercase    :				str       		=							(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          lowercase    :				Tuple       		=							self.vqvae.quantize.get_codebook_entry(_a	,    shape=_a				)
          lowercase    :				Any       		=							self.vqvae.decode(_a	,    force_not_quantize=_a				).sample

          lowercase    :				List[Any]       		=							(image / 2 + 0.5).clamp(0	,    1				)
          lowercase    :				Dict       		=							image.cpu().permute(0	,    2	,    3	,    1				).numpy()

          if output_type == "pil":
                 lowercase    :				List[str]       		=							self.numpy_to_pil(_a				)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a				):
          lowercase		,    lowercase    :				Dict       		=							torch.sort(_a	,    1	,    descending=_a				)
          lowercase    :				List[Any]       		=							torch.exp(_a				)
          lowercase    :				Union[str, Any]       		=							sorted_p_x_0.cumsum(dim=1				) < truncation_rate

          # Ensure that at least the largest probability is not zeroed out
          lowercase    :				List[str]       		=							torch.full_like(keep_mask[:, 0:1, :]	,    _a				)
          lowercase    :				List[Any]       		=							torch.cat((all_true, keep_mask)	,    dim=1				)
          lowercase    :				Dict       		=							keep_mask[:, :-1, :]

          lowercase    :				Optional[int]       		=							keep_mask.gather(1	,    indices.argsort(1				)				)

          lowercase    :				Union[str, Any]       		=							log_p_x_0.clone()

          lowercase    :				Union[str, Any]       		=							-torch.inf  # -inf = log(0)

          return rv




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


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


