





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        __lowerCamelCase (							__a       :int   )		->    Optional[Any]:






  """simple docstring"""

  A__  				=       image.size
  A__  				=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
  A__  				=       image.resize((w, h)   ,					resample=PIL_INTERPOLATION["""lanczos"""]   )
  A__  				=       np.array(_UpperCAmelCase   ).astype(np.floataa   ) / 2_5_5.0
  A__  				=       image[None].transpose(0   ,					3   ,					1   ,					2   )
  A__  				=       torch.from_numpy(_UpperCAmelCase   )
  return 2.0 * image - 1.0






class   A      (SCREAMING_SNAKE_CASE__   ):
   '''simple docstring'''




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







     """simple docstring"""






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







   @torch.no_grad()
   def __call__(      self       :   List[Any]							,     __lowerCAmelCase       :   Union[torch.Tensor, PIL.Image.Image] = None							,     __lowerCAmelCase       :   Optional[int] = 1							,     __lowerCAmelCase       :   Optional[int] = 1_00							,     __lowerCAmelCase       :   Optional[float] = 0.0							,     __lowerCAmelCase       :   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,     __lowerCAmelCase       :   Optional[str] = "pil"							,     __lowerCAmelCase       :   bool = True							,     )     ->     Union[Tuple, ImagePipelineOutput]:







     """simple docstring"""






     if isinstance(__lowerCAmelCase							,     PIL.Image.Image       ):
       A__  				=       1
     elif isinstance(__lowerCAmelCase							,     torch.Tensor       ):
       A__  				=       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       ):
       A__  				=       preprocess(__lowerCAmelCase       )

     A__  				=       image.shape[-2:]

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

     A__  				=       randn_tensor(__lowerCAmelCase							,     generator=__lowerCAmelCase							,     device=self.device							,     dtype=__lowerCAmelCase       )

     A__  				=       image.to(device=self.device							,     dtype=__lowerCAmelCase       )

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

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

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

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

     if output_type == "pil":
       A__  				=       self.numpy_to_pil(__lowerCAmelCase       )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=__lowerCAmelCase       )










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


A   : Dict			=				logging.get_logger(__name__)

A   : Dict			=				{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   '''pegasus'''
			__lowerCamelCase     :	Any          =   ['''past_key_values''']
			__lowerCamelCase     :	List[str]          =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(      self       :   List[Any]							,     __lowerCAmelCase       :   str=5_02_65							,     __lowerCAmelCase       :   Dict=10_24							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Any=40_96							,     __lowerCAmelCase       :   Optional[Any]=16							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[int]=40_96							,     __lowerCAmelCase       :   Dict=16							,     __lowerCAmelCase       :   Optional[int]=0.0							,     __lowerCAmelCase       :   Optional[Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=True							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   str="gelu"							,     __lowerCAmelCase       :   Union[str, Any]=10_24							,     __lowerCAmelCase       :   Union[str, Any]=0.1							,     __lowerCAmelCase       :   Dict=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0_2							,     __lowerCAmelCase       :   List[Any]=0							,     __lowerCAmelCase       :   str=False							,     __lowerCAmelCase       :   int=0							,     __lowerCAmelCase       :   Optional[Any]=1							,     __lowerCAmelCase       :   Optional[int]=1							,     **__lowerCAmelCase       :   Dict							,     )     ->     Dict:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       max_position_embeddings
					A__  				=       d_model
					A__  				=       encoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       encoder_attention_heads
					A__  				=       decoder_ffn_dim
					A__  				=       decoder_layers
					A__  				=       decoder_attention_heads
					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       activation_function
					A__  				=       init_std
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop
					A__  				=       use_cache
					A__  				=       encoder_layers
					A__  				=       scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     is_encoder_decoder=__lowerCAmelCase							,     decoder_start_token_id=__lowerCAmelCase							,     forced_eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase							,     )






			@property
			def        a_	(      self       :   Dict       )     ->     int:







					"""simple docstring"""






					return self.encoder_attention_heads







			@property
			def        a_	(      self       :   Optional[Any]       )     ->     int:







					"""simple docstring"""






					return self.d_model



