


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       Dict	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Dict		):





            """simple docstring"""



            super().__init__()
            self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)




     @torch.no_grad()
     def __call__( self       :       Tuple	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,      UpperCamelCase       :       int = 10_00	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      ):





            """simple docstring"""



            if isinstance(self.unet.config.sample_size	,      UpperCamelCase		):
                   _lowercase					:				str           =			(
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
            else:
                   _lowercase					:				Optional[Any]           =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   _lowercase					:				Optional[Any]           =			randn_tensor(UpperCamelCase	,      generator=UpperCamelCase		)
                   _lowercase					:				Optional[int]           =			image.to(self.device		)
            else:
                   _lowercase					:				Tuple           =			randn_tensor(UpperCamelCase	,      generator=UpperCamelCase	,      device=self.device		)

            # set step values
            self.scheduler.set_timesteps(UpperCamelCase		)

            for t in self.progress_bar(self.scheduler.timesteps		):
                   # 1. predict noise model_output
                   _lowercase					:				Tuple           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample

                   # 2. compute previous image: x_t -> x_t-1
                   _lowercase					:				Optional[int]           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      generator=UpperCamelCase		).prev_sample

            _lowercase					:				str           =			(image / 2 + 0.5).clamp(0	,      1		)
            _lowercase					:				List[str]           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
            if output_type == "pil":
                   _lowercase					:				Optional[Any]           =			self.numpy_to_pil(UpperCamelCase		)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=UpperCamelCase		)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__				       =			{
    'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
    'tokenization_realm': ['RealmTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			['RealmTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RealmEmbedder',
       'RealmForOpenQA',
       'RealmKnowledgeAugEncoder',
       'RealmPreTrainedModel',
       'RealmReader',
       'RealmScorer',
       'load_tf_weights_in_realm',
   ]
   UpperCamelCase__				       =			['RealmRetriever']


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)