





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






class 							__lowercase    ( lowerCamelCase__	, lowerCamelCase__  ):







	'''simple docstring'''

	@register_to_config
	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None	)							->							Optional[int]:







				'''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(snake_case__       ,snake_case__	)
				else:
							__lowercase			  =							None

				__lowercase			  =							torch.nn.Parameter(snake_case__	)






class 							__lowercase    ( lowerCamelCase__  ):







	'''simple docstring'''

	a      :			VQModel
	a      :			CLIPTextModel
	a      :			CLIPTokenizer
	a      :			TransformeraDModel
	a      :			LearnedClassifierFreeSamplingEmbeddings
	a      :			VQDiffusionScheduler


	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





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

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

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

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

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

				return prompt_embeds


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







				'''simple docstring'''





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

				__lowercase			  =							batch_size * num_images_per_prompt

				__lowercase			  =							guidance_scale > 1.0

				__lowercase			  =							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

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

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

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

				__lowercase			  =							latents

				for i, t in enumerate(self.progress_bar(snake_case__	)	):
							# 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(snake_case__       ,encoder_hidden_states=snake_case__       ,timestep=snake_case__	).sample

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

							__lowercase			  =							self.truncate(snake_case__       ,snake_case__	)

							# 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(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__	)

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=snake_case__	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							torch.FloatTensor:







				'''simple docstring'''





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












'''simple docstring'''



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=sys.maxsize	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							'''bilinear'''
				__lowercase			  =							max_size
				__lowercase			  =							short_edge_length


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							[]
				for img in imgs:
							__lowercase  ,						__lowercase			  =							img.shape[:2]
							# later: provide list and randomly choose index for resize
							__lowercase			  =							np.random.randint(self.short_edge_length[0]       ,self.short_edge_length[1] + 1	)
							if size == 0:
										return img
							__lowercase			  =							size * 1.0 / min(_lowerCamelCase       ,_lowerCamelCase	)
							if h < w:
										__lowercase  ,						__lowercase			  =							size, scale * w
							else:
										__lowercase  ,						__lowercase			  =							scale * h, size
							if max(_lowerCamelCase       ,_lowerCamelCase	) > self.max_size:
										__lowercase			  =							self.max_size * 1.0 / max(_lowerCamelCase       ,_lowerCamelCase	)
										__lowercase			  =							newh * scale
										__lowercase			  =							neww * scale
							__lowercase			  =							int(neww + 0.5	)
							__lowercase			  =							int(newh + 0.5	)

							if img.dtype == np.uinta:
										__lowercase			  =							Image.fromarray(_lowerCamelCase	)
										__lowercase			  =							pil_image.resize((neww, newh)       ,PILImageResampling.BILINEAR	)
										__lowercase			  =							np.asarray(_lowerCamelCase	)
							else:
										__lowercase			  =							img.permute(2       ,0       ,1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
										__lowercase			  =							nn.functional.interpolate(
										    _lowerCamelCase       ,(newh, neww)       ,mode=self.interp_method       ,align_corners=_lowerCamelCase	).squeeze(0	)
							img_augs.append(_lowerCamelCase	)

				return img_augs






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST	)
				__lowercase			  =							cfg.INPUT.FORMAT
				__lowercase			  =							cfg.SIZE_DIVISIBILITY
				__lowercase			  =							cfg.PAD_VALUE
				__lowercase			  =							cfg.INPUT.MAX_SIZE_TEST
				__lowercase			  =							cfg.MODEL.DEVICE
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							lambda _lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							tuple(max(_lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
				__lowercase			  =							[im.shape[-2:] for im in images]
				__lowercase			  =							[
				    nn.functional.pad(
				        _lowerCamelCase       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
				    for size, im in zip(_lowerCamelCase       ,_lowerCamelCase	)
				]

				return torch.stack(_lowerCamelCase	), torch.tensor(_lowerCamelCase	)


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Tuple:







				'''simple docstring'''





				with torch.no_grad():
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							[images]
							if single_image:
										assert len(_lowerCamelCase	) == 1
							for i in range(len(_lowerCamelCase	)	):
										if isinstance(images[i]       ,torch.Tensor	):
													images.insert(_lowerCamelCase       ,images.pop(_lowerCamelCase	).to(self.device	).float()	)
										elif not isinstance(images[i]       ,torch.Tensor	):
													images.insert(
													    _lowerCamelCase       ,torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase	)       ,input_format=self.input_format	)	)
													    .to(self.device	)
													    .float()       ,)
            # resize smallest edge
							__lowercase			  =							torch.tensor([im.shape[:2] for im in images]	)
							__lowercase			  =							self.aug(_lowerCamelCase	)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							__lowercase			  =							[self.normalizer(_lowerCamelCase	) for x in images]
							# now pad them to do the following operations
							__lowercase  ,						__lowercase			  =							self.pad(_lowerCamelCase	)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							__lowercase			  =							torch.true_divide(_lowerCamelCase       ,_lowerCamelCase	)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Tuple			):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Tuple[int, int]			):
			assert torch.isfinite(lowerCamelCase_			).all(), "Box tensor contains infinite or NaN!"
			__lowercase  ,						__lowercase			  =							box_size
			tensor[:, 0].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 1].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 2].clamp_(min=0						,    max=lowerCamelCase_			)
			tensor[:, 3].clamp_(min=0						,    max=lowerCamelCase_			)





