


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


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







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):





     @register_to_config
     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None       )     ->   str:
      super().__init__()

      _lowercase							      =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							      =torch.zeros(UpperCAmelCase		,     UpperCAmelCase       )
      else:
       _lowercase							      =None

      _lowercase							      =torch.nn.Parameter(UpperCAmelCase       )







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			42
     SCREAMING_SNAKE_CASE__      			=			42
     SCREAMING_SNAKE_CASE__      			=			42
     SCREAMING_SNAKE_CASE__      			=			42
     SCREAMING_SNAKE_CASE__      			=			42
     SCREAMING_SNAKE_CASE__      			=			42





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   int:
      super().__init__()

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =len(UpperCAmelCase       ) if isinstance(UpperCAmelCase		,     UpperCAmelCase       ) else 1

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

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       _lowercase							      =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							      =text_input_ids[:, : self.tokenizer.model_max_length]
      _lowercase							      =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							      =prompt_embeds / prompt_embeds.norm(dim=-1		,     keepdim=UpperCAmelCase       )

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

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

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

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

      return prompt_embeds





     @torch.no_grad()
     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase = 1_0_0		,     UpperCAmelCase = 5.0		,     UpperCAmelCase = 1.0		,     UpperCAmelCase = 1		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "pil"		,     UpperCAmelCase = True		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     )     ->   Union[ImagePipelineOutput, Tuple]:
      if isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
       _lowercase							      =1
      elif isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
       _lowercase							      =len(UpperCAmelCase       )
      else:
       raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase       )}"       )

      _lowercase							      =batch_size * num_images_per_prompt

      _lowercase							      =guidance_scale > 1.0

      _lowercase							      =self._encode_prompt(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )

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

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

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

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

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

      _lowercase							      =latents

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

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

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

       _lowercase							      =self.truncate(UpperCAmelCase		,     UpperCAmelCase       )

       # remove `log(0)`'s (`-inf`s)
       _lowercase							      =model_output.clamp(-7_0       )

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

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

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

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

      if output_type == "pil":
       _lowercase							      =self.numpy_to_pil(UpperCAmelCase       )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   torch.FloatTensor:
      _lowercase      ,    _lowercase							      =torch.sort(UpperCAmelCase		,     1		,     descending=UpperCAmelCase       )
      _lowercase							      =torch.exp(UpperCAmelCase       )
      _lowercase							      =sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

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

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

      _lowercase							      =log_p_x_0.clone()

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

      return rv





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

