

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 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		42
			SCREAMING_SNAKE_CASE							     =		42





			def __init__(	self      ,						__snake_case      ,						__snake_case      )     ->				List[Any]:




						'''simple docstring'''
						super().__init__()

						__a						   =PNDMScheduler.from_config(scheduler.config      )

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





			@torch.no_grad()
			def __call__(	self      ,						__snake_case = 1      ,						__snake_case = 50      ,						__snake_case = None      ,						__snake_case = "pil"      ,						__snake_case = True      ,						**__snake_case      ,						)     ->				Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a						   =randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,						generator=__snake_case      ,						device=self.device      ,						)

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

									__a						   =self.scheduler.step(__snake_case      ,						__snake_case      ,						__snake_case      ).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(__snake_case      )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case      )









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase      :    Union[str, Any]						       =    {
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    str						       =    ["ErnieMTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    Optional[Any]						       =    [
						    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ErnieMForMultipleChoice",
						    "ErnieMForQuestionAnswering",
						    "ErnieMForSequenceClassification",
						    "ErnieMForTokenClassification",
						    "ErnieMModel",
						    "ErnieMPreTrainedModel",
						    "ErnieMForInformationExtraction",
						]


if TYPE_CHECKING:
						from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ernie_m import ErnieMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ernie_m import (
												    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ErnieMForInformationExtraction,
												    ErnieMForMultipleChoice,
												    ErnieMForQuestionAnswering,
												    ErnieMForSequenceClassification,
												    ErnieMForTokenClassification,
												    ErnieMModel,
												    ErnieMPreTrainedModel,
												)


else:
						import sys

						_lowerCAmelCase      :    Tuple						       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






