






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 a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						@register_to_config
						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     bool				,							lowerCamelCase__ :     Optional[int] = None				,							lowerCamelCase__ :     Optional[int] = None				)    ->						List[Any]:
													"""simple docstring"""


													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(lowerCamelCase__				,							lowerCamelCase__				)
													else:
																				__lowercase      =    None

													__lowercase      =    torch.nn.Parameter(lowerCamelCase__				)







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       VQModel
						UpperCamelCase_	:       CLIPTextModel
						UpperCamelCase_	:       CLIPTokenizer
						UpperCamelCase_	:       TransformeraDModel
						UpperCamelCase_	:       LearnedClassifierFreeSamplingEmbeddings
						UpperCamelCase_	:       VQDiffusionScheduler






						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     VQModel				,							lowerCamelCase__ :     CLIPTextModel				,							lowerCamelCase__ :     CLIPTokenizer				,							lowerCamelCase__ :     TransformeraDModel				,							lowerCamelCase__ :     VQDiffusionScheduler				,							lowerCamelCase__ :     LearnedClassifierFreeSamplingEmbeddings				,							)    ->						Dict:
													"""simple docstring"""


													super().__init__()

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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Dict				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    len(lowerCamelCase__				) if isinstance(lowerCamelCase__				,							lowerCamelCase__				) else 1

													# get prompt text embeddings
													__lowercase      =    self.tokenizer(
													    lowerCamelCase__				,							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=lowerCamelCase__				)

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

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

																				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																				__lowercase      =    negative_prompt_embeds.shape[1]
																				__lowercase      =    negative_prompt_embeds.repeat(1				,							lowerCamelCase__				,							1				)
																				__lowercase      =    negative_prompt_embeds.view(batch_size * num_images_per_prompt				,							lowerCamelCase__				,							-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 :     Optional[int]				,							lowerCamelCase__ :     Union[str, List[str]]				,							lowerCamelCase__ :     int = 100				,							lowerCamelCase__ :     float = 5.0				,							lowerCamelCase__ :     float = 1.0				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							lowerCamelCase__ :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,							lowerCamelCase__ :     int = 1				,							)    ->						Union[ImagePipelineOutput, Tuple]:
													"""simple docstring"""


													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    1
													elif isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    len(lowerCamelCase__				)
													else:
																				raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__				)}'				)

													__lowercase      =    batch_size * num_images_per_prompt

													__lowercase      =    guidance_scale > 1.0

													__lowercase      =    self._encode_prompt(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

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

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

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

													__lowercase      =    latents

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

																				__lowercase      =    self.truncate(lowerCamelCase__				,							lowerCamelCase__				)

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

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

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

													__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(lowerCamelCase__				,							shape=lowerCamelCase__				)
													__lowercase      =    self.vqvae.decode(lowerCamelCase__				,							force_not_quantize=lowerCamelCase__				).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(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     torch.FloatTensor				,							lowerCamelCase__ :     float				)    ->						torch.FloatTensor:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    torch.sort(lowerCamelCase__				,							1				,							descending=lowerCamelCase__				)
													__lowercase      =    torch.exp(lowerCamelCase__				)
													__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, :]				,							lowerCamelCase__				)
													__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












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


													__lowercase      =    self.unet.config.sample_size
													__lowercase      =    (batch_size, 3, img_size, img_size)

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






