






'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase							={
    "configuration_realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig"],
    "tokenization_realm": ["RealmTokenizer"],
}

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

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

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

										self.register_modules(unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										a_									=   (image / 2 + 0.5).clamp(0   , 1     )
										a_									=   image.cpu().permute(0   , 2   , 3   , 1     ).numpy()
										if output_type == "pil":
															a_									=   self.numpy_to_pil(UpperCAmelCase__     )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

