





"""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


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



class 	lowerCAmelCase					(      snake_case			,	snake_case							):






       @register_to_config
       def __init__(   self    ,  a__    ,  a__ = None    ,  a__ = None     ):
              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(a__    ,  a__     )
              else:
                     _UpperCAmelCase     =     None

              _UpperCAmelCase     =     torch.nn.Parameter(a__     )



class 	lowerCAmelCase					(      snake_case							):
       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 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     len(a__     ) if isinstance(a__    ,  a__     ) else 1

              # get prompt text embeddings
              _UpperCAmelCase     =     self.tokenizer(
                  a__    ,  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=a__     )

              # duplicate text embeddings for each generation per prompt
              _UpperCAmelCase     =     prompt_embeds.repeat_interleave(a__    ,  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(a__    ,  1    ,  1     )
                     else:
                            _UpperCAmelCase     =     [''] * batch_size

                            _UpperCAmelCase     =     text_input_ids.shape[-1]
                            _UpperCAmelCase     =     self.tokenizer(
                                a__    ,  padding='max_length'    ,  max_length=a__    ,  truncation=a__    ,  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=a__     )

                     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                     _UpperCAmelCase     =     negative_prompt_embeds.shape[1]
                     _UpperCAmelCase     =     negative_prompt_embeds.repeat(1    ,  a__    ,  1     )
                     _UpperCAmelCase     =     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
                     _UpperCAmelCase     =     torch.cat([negative_prompt_embeds, prompt_embeds]     )

              return prompt_embeds






       @torch.no_grad()
       def __call__(   self    ,  a__    ,  a__ = 1_00    ,  a__ = 5.0    ,  a__ = 1.0    ,  a__ = 1    ,  a__ = None    ,  a__ = None    ,  a__ = "pil"    ,  a__ = True    ,  a__ = None    ,  a__ = 1    ,  ):
              if isinstance(a__    ,  a__     ):
                     _UpperCAmelCase     =     1
              elif isinstance(a__    ,  a__     ):
                     _UpperCAmelCase     =     len(a__     )
              else:
                     raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a__     )}"""     )

              _UpperCAmelCase     =     batch_size * num_images_per_prompt

              _UpperCAmelCase     =     guidance_scale > 1.0

              _UpperCAmelCase     =     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

              _UpperCAmelCase     =     (batch_size, self.transformer.num_latent_pixels)
              if latents is None:
                     _UpperCAmelCase     =     self.transformer.num_vector_embeds - 1
                     _UpperCAmelCase     =     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)."""     )
                     _UpperCAmelCase     =     latents.to(self.device     )

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

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

              _UpperCAmelCase     =     latents

              for i, t in enumerate(self.progress_bar(a__     )     ):
                     # 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(a__    ,  encoder_hidden_states=a__    ,  timestep=a__     ).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(a__    ,  dim=1    ,  keepdim=a__     )

                     _UpperCAmelCase     =     self.truncate(a__    ,  a__     )

                     # 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(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__     )

              _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(a__    ,  shape=a__     )
              _UpperCAmelCase     =     self.vqvae.decode(a__    ,  force_not_quantize=a__     ).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(a__     )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=a__     )






       def 							__A (   self    ,  a__    ,  a__     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     torch.sort(a__    ,  1    ,  descending=a__     )
              _UpperCAmelCase     =     torch.exp(a__     )
              _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, :]    ,  a__     )
              _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













"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       for param in module.parameters():
              _UpperCAmelCase     =     False

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              _UpperCAmelCase     =     'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'							)
       return device

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     plt.imshow(SCREAMING_SNAKE_CASE							)
       fig.axes.get_xaxis().set_visible(SCREAMING_SNAKE_CASE							)
       fig.axes.get_yaxis().set_visible(SCREAMING_SNAKE_CASE							)
       plt.show()


def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     datetime.now()
       _UpperCAmelCase     =     current_time.strftime('%H:%M:%S'							)
       return timestamp






