
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


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






class   UpperCAmelCase		(						A_       ,A_ ):



   @register_to_config
   def __init__(self		:					Dict    ,						snake_case__		:					bool    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None )		->	List[Any]:


          '''simple docstring'''


          super().__init__()

          snake_case       :   Optional[int]							=      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"

                 snake_case       :   Optional[int]							=      torch.zeros(snake_case__    ,						snake_case__ )
          else:
                 snake_case       :   Any							=      None

          snake_case       :   Optional[Any]							=      torch.nn.Parameter(snake_case__ )






class   UpperCAmelCase		(						A_ ):
   A__					:		VQModel
   A__					:		CLIPTextModel
   A__					:		CLIPTokenizer
   A__					:		TransformeraDModel
   A__					:		LearnedClassifierFreeSamplingEmbeddings
   A__					:		VQDiffusionScheduler



   def __init__(self		:					str    ,						snake_case__		:					VQModel    ,						snake_case__		:					CLIPTextModel    ,						snake_case__		:					CLIPTokenizer    ,						snake_case__		:					TransformeraDModel    ,						snake_case__		:					VQDiffusionScheduler    ,						snake_case__		:					LearnedClassifierFreeSamplingEmbeddings    ,						)		->	List[Any]:


          '''simple docstring'''


          super().__init__()

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Any    ,						snake_case__		:					int    ,						snake_case__		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      len(snake_case__ ) if isinstance(snake_case__    ,						snake_case__ ) else 1

          # get prompt text embeddings
          snake_case       :   Optional[Any]							=      self.tokenizer(
              snake_case__    ,						padding="max_length"    ,						max_length=self.tokenizer.model_max_length    ,						return_tensors="pt"    ,						)
          snake_case       :   Dict							=      text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 snake_case       :   List[str]							=      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}""" )
                 snake_case       :   str							=      text_input_ids[:, : self.tokenizer.model_max_length]
          snake_case       :   str							=      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
          snake_case       :   Dict							=      prompt_embeds / prompt_embeds.norm(dim=-1    ,						keepdim=snake_case__ )

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

          if do_classifier_free_guidance:
                 if self.learned_classifier_free_sampling_embeddings.learnable:
                        snake_case       :   Optional[int]							=      self.learned_classifier_free_sampling_embeddings.embeddings
                        snake_case       :   Any							=      negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__    ,						1    ,						1 )
                 else:
                        snake_case       :   Optional[int]							=      [""] * batch_size

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

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 snake_case       :   Dict							=      negative_prompt_embeds.shape[1]
                 snake_case       :   Any							=      negative_prompt_embeds.repeat(1    ,						snake_case__    ,						1 )
                 snake_case       :   List[Any]							=      negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,						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
                 snake_case       :   Tuple							=      torch.cat([negative_prompt_embeds, prompt_embeds] )

          return prompt_embeds



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


          '''simple docstring'''


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

          snake_case       :   List[str]							=      batch_size * num_images_per_prompt

          snake_case       :   Union[str, Any]							=      guidance_scale > 1.0

          snake_case       :   Optional[int]							=      self._encode_prompt(snake_case__    ,						snake_case__    ,						snake_case__ )

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(snake_case__    ,						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(snake_case__ )}.""" )

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

          snake_case       :   Tuple							=      (batch_size, self.transformer.num_latent_pixels)
          if latents is None:
                 snake_case       :   Optional[int]							=      self.transformer.num_vector_embeds - 1
                 snake_case       :   Optional[int]							=      torch.full(snake_case__    ,						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).""" )
                 snake_case       :   List[str]							=      latents.to(self.device )

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

          snake_case       :   Optional[int]							=      self.scheduler.timesteps.to(self.device )

          snake_case       :   List[str]							=      latents

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

                 # predict the un-noised image
                 # model_output == `log_p_x_0`
                 snake_case       :   Tuple							=      self.transformer(snake_case__    ,						encoder_hidden_states=snake_case__    ,						timestep=snake_case__ ).sample

                 if do_classifier_free_guidance:
                        snake_case				,					snake_case       :   Optional[Any]							=      model_output.chunk(2 )
                        snake_case       :   List[Any]							=      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                        model_output -= torch.logsumexp(snake_case__    ,						dim=1    ,						keepdim=snake_case__ )

                 snake_case       :   Tuple							=      self.truncate(snake_case__    ,						snake_case__ )

                 # remove `log(0)`'s (`-inf`s)
                 snake_case       :   List[str]							=      model_output.clamp(-70 )

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

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

          snake_case       :   List[Any]							=      self.vqvae.config.vq_embed_dim
          snake_case       :   List[str]							=      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          snake_case       :   List[str]							=      self.vqvae.quantize.get_codebook_entry(snake_case__    ,						shape=snake_case__ )
          snake_case       :   Optional[Any]							=      self.vqvae.decode(snake_case__    ,						force_not_quantize=snake_case__ ).sample

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

          if output_type == "pil":
                 snake_case       :   int							=      self.numpy_to_pil(snake_case__ )

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					float )		->	torch.FloatTensor:


          '''simple docstring'''


          snake_case				,					snake_case       :   Optional[int]							=      torch.sort(snake_case__    ,						1    ,						descending=snake_case__ )
          snake_case       :   List[Any]							=      torch.exp(snake_case__ )
          snake_case       :   List[Any]							=      sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

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

          snake_case       :   Tuple							=      keep_mask.gather(1    ,						indices.argsort(1 ) )

          snake_case       :   Any							=      log_p_x_0.clone()

          snake_case       :   List[Any]							=      -torch.inf  # -inf = log(0)

          return rv









def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
 else:
  return a * actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if b < 0:
  return 1 / actual_power(__snake_case     ,						__snake_case						)
 return actual_power(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  print(power(-2, -3))

