



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





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         super().__init__()
         self.register_modules(vqvae=_lowerCAmelCase       ,     unet=_lowerCAmelCase       ,     scheduler=_lowerCAmelCase			)





   @torch.no_grad()
   def __call__(	self       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = 100       ,     _lowerCAmelCase = 0.0       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = "pil"       ,     _lowerCAmelCase = True       ,     ):
         if isinstance(_lowerCAmelCase       ,     PIL.Image.Image			):
               _lowerCAmelCase 			=  1
         elif isinstance(_lowerCAmelCase       ,     torch.Tensor			):
               _lowerCAmelCase 			=  image.shape[0]
         else:
               raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCAmelCase			)}'''			)

         if isinstance(_lowerCAmelCase       ,     PIL.Image.Image			):
               _lowerCAmelCase 			=  preprocess(_lowerCAmelCase			)

         _lowerCAmelCase			,   _lowerCAmelCase 			=  image.shape[-2:]

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

         _lowerCAmelCase 			=  randn_tensor(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     device=self.device       ,     dtype=_lowerCAmelCase			)

         _lowerCAmelCase 			=  image.to(device=self.device       ,     dtype=_lowerCAmelCase			)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(_lowerCAmelCase       ,     device=self.device			)
         _lowerCAmelCase 			=  self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         _lowerCAmelCase 			=  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 			=  '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
         _lowerCAmelCase 			=  {}
         if accepts_eta:
               _lowerCAmelCase 			=  eta

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

         # decode the image latents with the VQVAE
         _lowerCAmelCase 			=  self.vqvae.decode(_lowerCAmelCase			).sample
         _lowerCAmelCase 			=  torch.clamp(_lowerCAmelCase       ,     -1.0       ,     1.0			)
         _lowerCAmelCase 			=  image / 2 + 0.5
         _lowerCAmelCase 			=  image.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()

         if output_type == "pil":
               _lowerCAmelCase 			=  self.numpy_to_pil(_lowerCAmelCase			)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase			)




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)