

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



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			@register_to_config
			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					bool       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[int] = None):
									super().__init__()

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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"

															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.zeros(lowerCAmelCase__       ,						lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	None

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.nn.Parameter(lowerCAmelCase__)







class   __lowercase    (			UpperCAmelCase_     ):




			"""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       ,						):
									super().__init__()

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else 1

									# get prompt text embeddings
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.tokenizer(
									    lowerCAmelCase__       ,						padding="max_length"       ,						max_length=self.tokenizer.model_max_length       ,						return_tensors="pt"       ,						)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	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}")
															SCREAMING_SNAKE_CASE_:			int  		=	text_input_ids[:, : self.tokenizer.model_max_length]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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
									SCREAMING_SNAKE_CASE_:			Tuple  		=	prompt_embeds / prompt_embeds.norm(dim=-1       ,						keepdim=lowerCAmelCase__)

									# duplicate text embeddings for each generation per prompt
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prompt_embeds.repeat_interleave(lowerCAmelCase__       ,						dim=0)

									if do_classifier_free_guidance:
															if self.learned_classifier_free_sampling_embeddings.learnable:
																					SCREAMING_SNAKE_CASE_:			int  		=	self.learned_classifier_free_sampling_embeddings.embeddings
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	negative_prompt_embeds.unsqueeze(0).repeat(lowerCAmelCase__       ,						1       ,						1)
															else:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[""] * batch_size

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

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

									return prompt_embeds



			@torch.no_grad()
			def __call__(			self  :					Any       ,						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       ,						):
									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	1
									elif isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(lowerCAmelCase__)
									else:
															raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__)}")

									SCREAMING_SNAKE_CASE_:			Any  		=	batch_size * num_images_per_prompt

									SCREAMING_SNAKE_CASE_:			List[str]  		=	guidance_scale > 1.0

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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

									SCREAMING_SNAKE_CASE_:			Dict  		=	(batch_size, self.transformer.num_latent_pixels)
									if latents is None:
															SCREAMING_SNAKE_CASE_:			str  		=	self.transformer.num_vector_embeds - 1
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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).")
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	latents.to(self.device)

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

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.scheduler.timesteps.to(self.device)

									SCREAMING_SNAKE_CASE_:			int  		=	latents

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

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

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

															SCREAMING_SNAKE_CASE_:			str  		=	self.truncate(lowerCAmelCase__       ,						lowerCAmelCase__)

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

															# compute the previous noisy sample x_t -> x_t-1
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	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__)

									SCREAMING_SNAKE_CASE_:			int  		=	self.vqvae.config.vq_embed_dim
									SCREAMING_SNAKE_CASE_:			Any  		=	(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__       ,						shape=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.vqvae.decode(lowerCAmelCase__       ,						force_not_quantize=lowerCAmelCase__).sample

									SCREAMING_SNAKE_CASE_:			List[str]  		=	(image / 2 + 0.5).clamp(0       ,						1)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	image.cpu().permute(0       ,						2       ,						3       ,						1).numpy()

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					float):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	torch.sort(lowerCAmelCase__       ,						1       ,						descending=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.exp(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	sorted_p_x_0.cumsum(dim=1) < truncation_rate

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

									SCREAMING_SNAKE_CASE_:			int  		=	keep_mask.gather(1       ,						indices.argsort(1))

									SCREAMING_SNAKE_CASE_:			str  		=	log_p_x_0.clone()

									SCREAMING_SNAKE_CASE_:			Any  		=	-torch.inf  # -inf = log(0)

									return rv









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray.")

									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True.")

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True.")

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






