


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__      : List[str]    ):
     __UpperCamelCase							,				__UpperCamelCase   							=image.size
     __UpperCamelCase							,				__UpperCamelCase   							=(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     __UpperCamelCase   							=image.resize((w, h)      ,					resample=PIL_INTERPOLATION['lanczos']    )
     __UpperCamelCase   							=np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 255.0
     __UpperCamelCase   							=image[None].transpose(0      ,					3      ,					1      ,					2    )
     __UpperCamelCase   							=torch.from_numpy(SCREAMING_SNAKE_CASE__    )
     return 2.0 * image - 1.0






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       )  ->	Dict:
         super().__init__()
         self.register_modules(vqvae=A_ ,       unet=A_ ,       scheduler=A_   )






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

         if isinstance(A_ ,       PIL.Image.Image   ):
              __UpperCamelCase   							=preprocess(A_   )

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

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

         __UpperCamelCase   							=randn_tensor(A_ ,       generator=A_ ,       device=self.device ,       dtype=A_   )

         __UpperCamelCase   							=image.to(device=self.device ,       dtype=A_   )

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

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

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

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

         if output_type == "pil":
              __UpperCamelCase   							=self.numpy_to_pil(A_   )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=A_   )









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


_A			=						logging.get_logger(__name__)

_A			=						{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "realm"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=128 ,       A_=12 ,       A_=12 ,       A_=8 ,       A_=3072 ,       A_="gelu_new" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=2 ,       A_=0.02 ,       A_=1E-12 ,       A_=256 ,       A_=10 ,       A_=1E-3 ,       A_=5 ,       A_=320 ,       A_=13353718 ,       A_=5000 ,       A_=1 ,       A_=0 ,       A_=2 ,       **A_ ,       )  ->	Dict:
         super().__init__(pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       **A_   )

         # Common config
         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=retriever_proj_size
         __UpperCamelCase   							=num_hidden_layers
         __UpperCamelCase   							=num_attention_heads
         __UpperCamelCase   							=num_candidates
         __UpperCamelCase   							=intermediate_size
         __UpperCamelCase   							=hidden_act
         __UpperCamelCase   							=hidden_dropout_prob
         __UpperCamelCase   							=attention_probs_dropout_prob
         __UpperCamelCase   							=initializer_range
         __UpperCamelCase   							=type_vocab_size
         __UpperCamelCase   							=layer_norm_eps

         # Reader config
         __UpperCamelCase   							=span_hidden_size
         __UpperCamelCase   							=max_span_width
         __UpperCamelCase   							=reader_layer_norm_eps
         __UpperCamelCase   							=reader_beam_size
         __UpperCamelCase   							=reader_seq_len

         # Retrieval config
         __UpperCamelCase   							=num_block_records
         __UpperCamelCase   							=searcher_beam_size





