



"""simple docstring"""






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def      _lowercase							(    __snake_case      )		->  Tuple:
			__lowerCAmelCase		,      __lowerCAmelCase :    Union[str, Any]				=			image.size
			__lowerCAmelCase		,      __lowerCAmelCase :    str				=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
			__lowerCAmelCase :    str				=			image.resize((w, h)   ,resample=PIL_INTERPOLATION["lanczos"]      )
			__lowerCAmelCase :    List[Any]				=			np.array(__snake_case      ).astype(np.floataa      ) / 255.0
			__lowerCAmelCase :    int				=			image[None].transpose(0   ,3   ,1   ,2      )
			__lowerCAmelCase :    Union[str, Any]				=			torch.from_numpy(__snake_case      )
			return 2.0 * image - 1.0





class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						VQModel			,     _SCREAMING_SNAKE_CASE:						UNetaDModel			,     _SCREAMING_SNAKE_CASE:						Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ]			,     )							->			Optional[int]:

				"""simple docstring"""

				super().__init__()
				self.register_modules(vqvae=_SCREAMING_SNAKE_CASE			,     unet=_SCREAMING_SNAKE_CASE			,     scheduler=_SCREAMING_SNAKE_CASE)







	@torch.no_grad()
	def __call__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						Union[torch.Tensor, PIL.Image.Image] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int] = 1			,     _SCREAMING_SNAKE_CASE:						Optional[int] = 100			,     _SCREAMING_SNAKE_CASE:						Optional[float] = 0.0			,     _SCREAMING_SNAKE_CASE:						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[str] = "pil"			,     _SCREAMING_SNAKE_CASE:						bool = True			,     )							->			Union[Tuple, ImagePipelineOutput]:

				"""simple docstring"""

				if isinstance(_SCREAMING_SNAKE_CASE			,     PIL.Image.Image):
							__lowerCAmelCase :    List[str]				=			1
				elif isinstance(_SCREAMING_SNAKE_CASE			,     torch.Tensor):
							__lowerCAmelCase :    Tuple				=			image.shape[0]
				else:
							raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_SCREAMING_SNAKE_CASE)}""")

				if isinstance(_SCREAMING_SNAKE_CASE			,     PIL.Image.Image):
							__lowerCAmelCase :    List[Any]				=			preprocess(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    int				=			image.shape[-2:]

				# in_channels should be 6: 3 for latents, 3 for low resolution image
				__lowerCAmelCase :    str				=			(batch_size, self.unet.config.in_channels // 2, height, width)
				__lowerCAmelCase :    str				=			next(self.unet.parameters()).dtype

				__lowerCAmelCase :    Any				=			randn_tensor(_SCREAMING_SNAKE_CASE			,     generator=_SCREAMING_SNAKE_CASE			,     device=self.device			,     dtype=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			image.to(device=self.device			,     dtype=_SCREAMING_SNAKE_CASE)

				# set timesteps and move to the correct device
				self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			,     device=self.device)
				__lowerCAmelCase :    int				=			self.scheduler.timesteps

				# scale the initial noise by the standard deviation required by the scheduler
				__lowerCAmelCase :    Optional[int]				=			latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				__lowerCAmelCase :    Optional[Any]				=			"eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
				__lowerCAmelCase :    Union[str, Any]				=			{}
				if accepts_eta:
							__lowerCAmelCase :    Union[str, Any]				=			eta

				for t in self.progress_bar(_SCREAMING_SNAKE_CASE):
							# concat latents and low resolution image in the channel dimension.
							__lowerCAmelCase :    Optional[Any]				=			torch.cat([latents, image]			,     dim=1)
							__lowerCAmelCase :    Optional[int]				=			self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							# predict the noise residual
							__lowerCAmelCase :    Union[str, Any]				=			self.unet(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE).sample
							# compute the previous noisy sample x_t -> x_t-1
							__lowerCAmelCase :    Tuple				=			self.scheduler.step(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE).prev_sample

				# decode the image latents with the VQVAE
				__lowerCAmelCase :    Any				=			self.vqvae.decode(_SCREAMING_SNAKE_CASE).sample
				__lowerCAmelCase :    Dict				=			torch.clamp(_SCREAMING_SNAKE_CASE			,     -1.0			,     1.0)
				__lowerCAmelCase :    Tuple				=			image / 2 + 0.5
				__lowerCAmelCase :    List[Any]				=			image.cpu().permute(0			,     2			,     3			,     1).numpy()

				if output_type == "pil":
							__lowerCAmelCase :    Union[str, Any]				=			self.numpy_to_pil(_SCREAMING_SNAKE_CASE)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE)




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Dict =       logging.get_logger(__name__)

__snake_case				:  int =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'visual_bert'

	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						Dict=3_0522			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=768			,     _SCREAMING_SNAKE_CASE:						List[str]=512			,     _SCREAMING_SNAKE_CASE:						Optional[int]=12			,     _SCREAMING_SNAKE_CASE:						List[str]=12			,     _SCREAMING_SNAKE_CASE:						Tuple=3072			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="gelu"			,     _SCREAMING_SNAKE_CASE:						Tuple=0.1			,     _SCREAMING_SNAKE_CASE:						str=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=512			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						Optional[int]=0.02			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1e-12			,     _SCREAMING_SNAKE_CASE:						List[str]=False			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Tuple=0			,     _SCREAMING_SNAKE_CASE:						Any=2			,     **_SCREAMING_SNAKE_CASE:						List[str]			,     )							->			str:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			vocab_size
				__lowerCAmelCase :    Tuple				=			max_position_embeddings
				__lowerCAmelCase :    Any				=			hidden_size
				__lowerCAmelCase :    Union[str, Any]				=			visual_embedding_dim
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Optional[int]				=			num_attention_heads
				__lowerCAmelCase :    str				=			intermediate_size
				__lowerCAmelCase :    int				=			hidden_act
				__lowerCAmelCase :    Union[str, Any]				=			hidden_dropout_prob
				__lowerCAmelCase :    int				=			attention_probs_dropout_prob
				__lowerCAmelCase :    List[Any]				=			initializer_range
				__lowerCAmelCase :    Dict				=			type_vocab_size
				__lowerCAmelCase :    Optional[int]				=			layer_norm_eps
				__lowerCAmelCase :    Union[str, Any]				=			bypass_transformer
				__lowerCAmelCase :    List[str]				=			special_visual_initialize