



"""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


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







class       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						@register_to_config
						def __init__(					self :List[str]   ,  lowercase_ :bool   ,  lowercase_ :Optional[int] = None   ,  lowercase_ :Optional[int] = None					)				->			List[Any]:
												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(lowercase_   ,  lowercase_					)
												else:
																		UpperCAmelCase             = None

												UpperCAmelCase             = torch.nn.Parameter(lowercase_					)







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42






						def __init__(					self :Union[str, Any]   ,  lowercase_ :VQModel   ,  lowercase_ :CLIPTextModel   ,  lowercase_ :CLIPTokenizer   ,  lowercase_ :TransformeraDModel   ,  lowercase_ :VQDiffusionScheduler   ,  lowercase_ :LearnedClassifierFreeSamplingEmbeddings   ,  )				->			Union[str, Any]:
												super().__init__()

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






						def 	UpperCAmelCase__     (					self :Tuple   ,  lowercase_ :List[Any]   ,  lowercase_ :str   ,  lowercase_ :Optional[Any]					)				->			Union[str, Any]:
												UpperCAmelCase             = len(lowercase_					) if isinstance(lowercase_   ,  lowercase_					) else 1

												# get prompt text embeddings
												UpperCAmelCase             = self.tokenizer(
												    lowercase_   ,  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=lowercase_					)

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

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

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		UpperCAmelCase             = negative_prompt_embeds.shape[1]
																		UpperCAmelCase             = negative_prompt_embeds.repeat(1   ,  lowercase_   ,  1					)
																		UpperCAmelCase             = negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,  lowercase_   ,  -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 :str   ,  lowercase_ :Union[str, List[str]]   ,  lowercase_ :int = 1_00   ,  lowercase_ :float = 5.0   ,  lowercase_ :float = 1.0   ,  lowercase_ :int = 1   ,  lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,  lowercase_ :Optional[torch.FloatTensor] = None   ,  lowercase_ :Optional[str] = "pil"   ,  lowercase_ :bool = True   ,  lowercase_ :Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,  lowercase_ :int = 1   ,  )				->			Union[ImagePipelineOutput, Tuple]:
												if isinstance(lowercase_   ,  lowercase_					):
																		UpperCAmelCase             = 1
												elif isinstance(lowercase_   ,  lowercase_					):
																		UpperCAmelCase             = len(lowercase_					)
												else:
																		raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_					)}"""					)

												UpperCAmelCase             = batch_size * num_images_per_prompt

												UpperCAmelCase             = guidance_scale > 1.0

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

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

												# 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(lowercase_   ,  lowercase_					).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(lowercase_   ,  device=self.device					)

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

												UpperCAmelCase             = latents

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

																		UpperCAmelCase             = self.truncate(lowercase_   ,  lowercase_					)

																		# 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(lowercase_   ,  timestep=lowercase_   ,  sample=lowercase_   ,  generator=lowercase_					).prev_sample

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

												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(lowercase_   ,  shape=lowercase_					)
												UpperCAmelCase             = self.vqvae.decode(lowercase_   ,  force_not_quantize=lowercase_					).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(lowercase_					)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_					)






						def 	UpperCAmelCase__     (					self :int   ,  lowercase_ :torch.FloatTensor   ,  lowercase_ :float					)				->			torch.FloatTensor:
												UpperCAmelCase      ,    UpperCAmelCase             = torch.sort(lowercase_   ,  1   ,  descending=lowercase_					)
												UpperCAmelCase             = torch.exp(lowercase_					)
												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, :]   ,  lowercase_					)
												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"""


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
       import ctypes
       import msvcrt  # noqa


       class 					snake_case		(							ctypes.Structure						):


          '''simple docstring'''

          A_						:	List[str]						 = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l''' )
    sys.stdout.flush()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h''' )
    sys.stdout.flush()





@contextmanager
def 					lowerCAmelCase				(    ):




  """simple docstring"""







  try:
    hide_cursor()
    yield
  finally:
    show_cursor()




