



'''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


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


class 	SCREAMING_SNAKE_CASE__					( _lowercase  ,      _lowercase       ):
	"""simple docstring"""


	@register_to_config
	def __init__(		self       , A       , A = None       , A = None      )			->				int:
				super().__init__()

				A:   Union[str, Any]								= 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"

							A:   int								= torch.zeros(_snake_case       , _snake_case      )
				else:
							A:   List[Any]								= None

				A:   Any								= torch.nn.Parameter(_snake_case      )







class 	SCREAMING_SNAKE_CASE__					( _lowercase       ):
	"""simple docstring"""


	A__     :     Tuple    =					42
	A__     :     Optional[int]    =					42
	A__     :     Union[str, Any]    =					42
	A__     :     List[Any]    =					42
	A__     :     int    =					42
	A__     :     Optional[Any]    =					42


	def __init__(		self       , A       , A       , A       , A       , A       , A       , )			->				Dict:
				super().__init__()

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


	def   a__			(		self       , A       , A       , A      )			->				Union[str, Any]:
				A:   Any								= len(_snake_case      ) if isinstance(_snake_case       , _snake_case      ) else 1

				# get prompt text embeddings
				A:   Any								= self.tokenizer(
				    _snake_case       , padding="""max_length"""       , max_length=self.tokenizer.model_max_length       , return_tensors="""pt"""       , )
				A:   int								= text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A:   List[Any]								= 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}'      )
							A:   List[Any]								= text_input_ids[:, : self.tokenizer.model_max_length]
				A:   int								= 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
				A:   List[Any]								= prompt_embeds / prompt_embeds.norm(dim=-1       , keepdim=_snake_case      )

				# duplicate text embeddings for each generation per prompt
				A:   List[Any]								= prompt_embeds.repeat_interleave(_snake_case       , dim=0      )

				if do_classifier_free_guidance:
							if self.learned_classifier_free_sampling_embeddings.learnable:
										A:   int								= self.learned_classifier_free_sampling_embeddings.embeddings
										A:   Tuple								= negative_prompt_embeds.unsqueeze(0      ).repeat(_snake_case       , 1       , 1      )
							else:
										A:   Tuple								= [""""""] * batch_size

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

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A:   Optional[Any]								= negative_prompt_embeds.shape[1]
							A:   str								= negative_prompt_embeds.repeat(1       , _snake_case       , 1      )
							A:   str								= negative_prompt_embeds.view(batch_size * num_images_per_prompt       , _snake_case       , -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
							A:   Any								= 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       , )			->				Union[ImagePipelineOutput, Tuple]:
				if isinstance(_snake_case       , _snake_case      ):
							A:   Tuple								= 1
				elif isinstance(_snake_case       , _snake_case      ):
							A:   str								= len(_snake_case      )
				else:
							raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_snake_case      )}'      )

				A:   str								= batch_size * num_images_per_prompt

				A:   List[str]								= guidance_scale > 1.0

				A:   Tuple								= self._encode_prompt(_snake_case       , _snake_case       , _snake_case      )

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

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

				A:   List[str]								= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
							A:   int								= self.transformer.num_vector_embeds - 1
							A:   Union[str, Any]								= torch.full(_snake_case       , _snake_case      ).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).'      )
							A:   List[Any]								= latents.to(self.device      )

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

				A:   Optional[Any]								= self.scheduler.timesteps.to(self.device      )

				A:   Dict								= latents

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

							# predict the un-noised image
							# model_output == `log_p_x_0`
							A:   Tuple								= self.transformer(_snake_case       , encoder_hidden_states=_snake_case       , timestep=_snake_case      ).sample

							if do_classifier_free_guidance:
										A				,		A:   List[str]								= model_output.chunk(2      )
										A:   int								= model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
										model_output -= torch.logsumexp(_snake_case       , dim=1       , keepdim=_snake_case      )

							A:   Optional[int]								= self.truncate(_snake_case       , _snake_case      )

							# remove `log(0)`'s (`-inf`s)
							A:   Union[str, Any]								= model_output.clamp(-70      )

							# compute the previous noisy sample x_t -> x_t-1
							A:   Dict								= self.scheduler.step(_snake_case       , timestep=_snake_case       , sample=_snake_case       , generator=_snake_case      ).prev_sample

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

				A:   List[Any]								= self.vqvae.config.vq_embed_dim
				A:   Tuple								= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				A:   Union[str, Any]								= self.vqvae.quantize.get_codebook_entry(_snake_case       , shape=_snake_case      )
				A:   int								= self.vqvae.decode(_snake_case       , force_not_quantize=_snake_case      ).sample

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

				if output_type == "pil":
							A:   Optional[int]								= self.numpy_to_pil(_snake_case      )

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_snake_case      )





	def   a__			(		self       , A       , A      )			->				torch.FloatTensor:
				A				,		A:   List[str]								= torch.sort(_snake_case       , 1       , descending=_snake_case      )
				A:   Optional[Any]								= torch.exp(_snake_case      )
				A:   List[Any]								= sorted_p_x_0.cumsum(dim=1      ) < truncation_rate

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

				A:   List[str]								= keep_mask.gather(1       , indices.argsort(1      )      )

				A:   Dict								= log_p_x_0.clone()

				A:   List[str]								= -torch.inf  # -inf = log(0)

				return rv







"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




