# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 			UpperCamelCase					(			_a			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_    )			->					Any:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=_a					,					scheduler=_a    )





     @torch.no_grad()
     def __call__(       self					,					A_ = 1					,					A_ = None					,					A_ = 50					,					A_ = "pil"					,					A_ = True					,					**A_					,					)			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					torch.randn(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,					generator=_a					,					)
           _lowerCamelCase           =					image.to(self.device    )

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

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

                 # 2. predict previous mean of image x_t-1 and add variance depending on eta
                 # eta corresponds to η in paper and should be between [0, 1]
                 # do x_t -> x_t-1
                 _lowerCamelCase           =					self.scheduler.step(_a					,					_a					,					_a    ).prev_sample

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

           if not return_dict:
                 return (image,), "This is a local test"

           return ImagePipelineOutput(images=_a    ), "This is a local test"




from typing import TYPE_CHECKING

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


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''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

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





