
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


_snake_case      = logging.get_logger(__name__)  # pylint: disable=invalid-name
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @register_to_config
 def __init__(						self			:			Any					, SCREAMING_SNAKE_CASE__			:			bool					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None   ):






      """simple docstring"""



      super().__init__()

      UpperCamelCase						    =					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"

           UpperCamelCase						    =					torch.zeros(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      else:
           UpperCamelCase						    =					None

      UpperCamelCase						    =					torch.nn.Parameter(SCREAMING_SNAKE_CASE__   )




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					VQModel
 SCREAMING_SNAKE_CASE_				:					CLIPTextModel
 SCREAMING_SNAKE_CASE_				:					CLIPTokenizer
 SCREAMING_SNAKE_CASE_				:					TransformeraDModel
 SCREAMING_SNAKE_CASE_				:					LearnedClassifierFreeSamplingEmbeddings
 SCREAMING_SNAKE_CASE_				:					VQDiffusionScheduler

 def __init__(						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			VQModel					, SCREAMING_SNAKE_CASE__			:			CLIPTextModel					, SCREAMING_SNAKE_CASE__			:			CLIPTokenizer					, SCREAMING_SNAKE_CASE__			:			TransformeraDModel					, SCREAMING_SNAKE_CASE__			:			VQDiffusionScheduler					, SCREAMING_SNAKE_CASE__			:			LearnedClassifierFreeSamplingEmbeddings					, ):






      """simple docstring"""



      super().__init__()

      self.register_modules(
          vqvae=SCREAMING_SNAKE_CASE__					, transformer=SCREAMING_SNAKE_CASE__					, text_encoder=SCREAMING_SNAKE_CASE__					, tokenizer=SCREAMING_SNAKE_CASE__					, scheduler=SCREAMING_SNAKE_CASE__					, learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					len(SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) else 1

      # get prompt text embeddings
      UpperCamelCase						    =					self.tokenizer(
          SCREAMING_SNAKE_CASE__					, padding='max_length'					, max_length=self.tokenizer.model_max_length					, return_tensors='pt'					, )
      UpperCamelCase						    =					text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           UpperCamelCase						    =					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}'   )
           UpperCamelCase						    =					text_input_ids[:, : self.tokenizer.model_max_length]
      UpperCamelCase						    =					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
      UpperCamelCase						    =					prompt_embeds / prompt_embeds.norm(dim=-1					, keepdim=SCREAMING_SNAKE_CASE__   )

      # duplicate text embeddings for each generation per prompt
      UpperCamelCase						    =					prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__					, dim=0   )

      if do_classifier_free_guidance:
           if self.learned_classifier_free_sampling_embeddings.learnable:
                UpperCamelCase						    =					self.learned_classifier_free_sampling_embeddings.embeddings
                UpperCamelCase						    =					negative_prompt_embeds.unsqueeze(0   ).repeat(SCREAMING_SNAKE_CASE__					, 1					, 1   )
           else:
                UpperCamelCase						    =					[''] * batch_size

                UpperCamelCase						    =					text_input_ids.shape[-1]
                UpperCamelCase						    =					self.tokenizer(
                    SCREAMING_SNAKE_CASE__					, padding='max_length'					, max_length=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, return_tensors='pt'					, )
                UpperCamelCase						    =					self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
                # See comment for normalizing text embeddings
                UpperCamelCase						    =					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					, keepdim=SCREAMING_SNAKE_CASE__   )

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase						    =					negative_prompt_embeds.shape[1]
           UpperCamelCase						    =					negative_prompt_embeds.repeat(1					, SCREAMING_SNAKE_CASE__					, 1   )
           UpperCamelCase						    =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					, SCREAMING_SNAKE_CASE__					, -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
           UpperCamelCase						    =					torch.cat([negative_prompt_embeds, prompt_embeds]   )

      return prompt_embeds

 @torch.no_grad()
 def __call__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, List[str]]					, SCREAMING_SNAKE_CASE__			:			int = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 5.0					, SCREAMING_SNAKE_CASE__			:			float = 1.0					, SCREAMING_SNAKE_CASE__			:			int = 1					, SCREAMING_SNAKE_CASE__			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[torch.FloatTensor] = None					, SCREAMING_SNAKE_CASE__			:			Optional[str] = "pil"					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, SCREAMING_SNAKE_CASE__			:			int = 1					, ):






      """simple docstring"""



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

      UpperCamelCase						    =					batch_size * num_images_per_prompt

      UpperCamelCase						    =					guidance_scale > 1.0

      UpperCamelCase						    =					self._encode_prompt(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

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

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

      UpperCamelCase						    =					(batch_size, self.transformer.num_latent_pixels)
      if latents is None:
           UpperCamelCase						    =					self.transformer.num_vector_embeds - 1
           UpperCamelCase						    =					torch.full(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).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).'   )
           UpperCamelCase						    =					latents.to(self.device   )

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

      UpperCamelCase						    =					self.scheduler.timesteps.to(self.device   )

      UpperCamelCase						    =					latents

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

           # predict the un-noised image
           # model_output == `log_p_x_0`
           UpperCamelCase						    =					self.transformer(SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, timestep=SCREAMING_SNAKE_CASE__   ).sample

           if do_classifier_free_guidance:
                UpperCamelCase    ,		UpperCamelCase						    =					model_output.chunk(2   )
                UpperCamelCase						    =					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                model_output -= torch.logsumexp(SCREAMING_SNAKE_CASE__					, dim=1					, keepdim=SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.truncate(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

           # remove `log(0)`'s (`-inf`s)
           UpperCamelCase						    =					model_output.clamp(-70   )

           # compute the previous noisy sample x_t -> x_t-1
           UpperCamelCase						    =					self.scheduler.step(SCREAMING_SNAKE_CASE__					, timestep=SCREAMING_SNAKE_CASE__					, sample=SCREAMING_SNAKE_CASE__					, generator=SCREAMING_SNAKE_CASE__   ).prev_sample

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

      UpperCamelCase						    =					self.vqvae.config.vq_embed_dim
      UpperCamelCase						    =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
      UpperCamelCase						    =					self.vqvae.quantize.get_codebook_entry(SCREAMING_SNAKE_CASE__					, shape=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.vqvae.decode(SCREAMING_SNAKE_CASE__					, force_not_quantize=SCREAMING_SNAKE_CASE__   ).sample

      UpperCamelCase						    =					(image / 2 + 0.5).clamp(0					, 1   )
      UpperCamelCase						    =					image.cpu().permute(0					, 2					, 3					, 1   ).numpy()

      if output_type == "pil":
           UpperCamelCase						    =					self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			torch.FloatTensor					, SCREAMING_SNAKE_CASE__			:			float   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					torch.sort(SCREAMING_SNAKE_CASE__					, 1					, descending=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.exp(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

      # Ensure that at least the largest probability is not zeroed out
      UpperCamelCase						    =					torch.full_like(keep_mask[:, 0:1, :]					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.cat((all_true, keep_mask)					, dim=1   )
      UpperCamelCase						    =					keep_mask[:, :-1, :]

      UpperCamelCase						    =					keep_mask.gather(1					, indices.argsort(1   )   )

      UpperCamelCase						    =					log_p_x_0.clone()

      UpperCamelCase						    =					-torch.inf  # -inf = log(0)

      return rv




def  __lowerCamelCase					( _lowercase			)       ->      list:
     UpperCamelCase						    =					len(_lowercase			)
     for _ in range(_lowercase			):
          for i in range(_ % 2						,     arr_size - 1						,     2			):
               if arr[i + 1] < arr[i]:
                    UpperCamelCase    ,		UpperCamelCase						    =					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _snake_case      = list(range(10, 0, -1))
 print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")


