"""simple docstring"""







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   @torch.no_grad()
   def __call__(	self ,				_SCREAMING_SNAKE_CASE = 1 ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = 50 ,				_SCREAMING_SNAKE_CASE = "pil" ,				_SCREAMING_SNAKE_CASE = True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,				generator=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					image.to(self.device	)

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

      for t in self.progress_bar(self.scheduler.timesteps	):
         # 1. predict noise model_output
         __lowerCamelCase    		=					self.unet(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	).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(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	).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(_SCREAMING_SNAKE_CASE	)

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

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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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