






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


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


class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):

	@register_to_config
	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None       )->					Any:







							'''simple docstring'''





							super().__init__()

							lowerCAmelCase__       =						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"

													lowerCAmelCase__       =						torch.zeros(__UpperCAmelCase		,    __UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						None

							lowerCAmelCase__       =						torch.nn.Parameter(__UpperCAmelCase       )





class      lowercase__     (			_UpperCAmelCase	):
	a_         =42
	a_         =42
	a_         =42
	a_         =42
	a_         =42
	a_         =42

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					Dict:







							'''simple docstring'''





							super().__init__()

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else 1

							# get prompt text embeddings
							lowerCAmelCase__       =						self.tokenizer(
							    __UpperCAmelCase		,    padding="max_length"		,    max_length=self.tokenizer.model_max_length		,    return_tensors="pt"		,    )
							lowerCAmelCase__       =						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													lowerCAmelCase__       =						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}"       )
													lowerCAmelCase__       =						text_input_ids[:, : self.tokenizer.model_max_length]
							lowerCAmelCase__       =						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
							lowerCAmelCase__       =						prompt_embeds / prompt_embeds.norm(dim=-1		,    keepdim=__UpperCAmelCase       )

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

							if do_classifier_free_guidance:
													if self.learned_classifier_free_sampling_embeddings.learnable:
																			lowerCAmelCase__       =						self.learned_classifier_free_sampling_embeddings.embeddings
																			lowerCAmelCase__       =						negative_prompt_embeds.unsqueeze(0       ).repeat(__UpperCAmelCase		,    1		,    1       )
													else:
																			lowerCAmelCase__       =						[""] * batch_size

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

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

							return prompt_embeds

	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 100		,    __UpperCAmelCase = 5.0		,    __UpperCAmelCase = 1.0		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





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

							lowerCAmelCase__       =						batch_size * num_images_per_prompt

							lowerCAmelCase__       =						guidance_scale > 1.0

							lowerCAmelCase__       =						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

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

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

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

							lowerCAmelCase__       =						latents

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

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

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

													lowerCAmelCase__       =						self.truncate(__UpperCAmelCase		,    __UpperCAmelCase       )

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

													# compute the previous noisy sample x_t -> x_t-1
													lowerCAmelCase__       =						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       )

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

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

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.sort(__UpperCAmelCase		,    1		,    descending=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.exp(__UpperCAmelCase       )
							lowerCAmelCase__       =						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

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

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

							lowerCAmelCase__       =						log_p_x_0.clone()

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

							return rv














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	a_							=    CLIPImageProcessor()
	a_							=    CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	a_							=    UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)






