
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				image.size
      UpperCamelCase__    :   Any					=				(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
      UpperCamelCase__    :   Union[str, Any]					=				image.resize((w, h) ,		resample=PIL_INTERPOLATION['''lanczos''']		)
      UpperCamelCase__    :   Optional[Any]					=				np.array(UpperCamelCase__		).astype(np.floataa		) / 255.0
      UpperCamelCase__    :   Tuple					=				image[None].transpose(0 ,		3 ,		1 ,		2		)
      UpperCamelCase__    :   int					=				torch.from_numpy(UpperCamelCase__		)
      return 2.0 * image - 1.0




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   Dict:




           """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						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = 1						,						__SCREAMING_SNAKE_CASE = 1_0_0						,						__SCREAMING_SNAKE_CASE = 0.0						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = "pil"						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Union[Tuple, ImagePipelineOutput]:




           """simple docstring"""







           if isinstance(__SCREAMING_SNAKE_CASE						,						PIL.Image.Image  ):
                 UpperCamelCase__    :   Union[str, Any]					=				1
           elif isinstance(__SCREAMING_SNAKE_CASE						,						torch.Tensor  ):
                 UpperCamelCase__    :   int					=				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  ):
                 UpperCamelCase__    :   Tuple					=				preprocess(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[Any]					=				image.shape[-2:]

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

           UpperCamelCase__    :   str					=				randn_tensor(__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						device=self.device						,						dtype=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				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  )
           UpperCamelCase__    :   List[str]					=				self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           UpperCamelCase__    :   Dict					=				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]
           UpperCamelCase__    :   str					=				'''eta''' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
           UpperCamelCase__    :   Optional[Any]					=				{}
           if accepts_eta:
                 UpperCamelCase__    :   Any					=				eta

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

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

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






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




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


