





'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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

class    lowerCAmelCase_		(		UpperCamelCase__		):
 __lowerCamelCase						:							Union[str, Any]         =					42
 __lowerCamelCase						:							int         =					42




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							int:
     super().__init__()
     self.register_modules(unet=_a			,     scheduler=_a		)





 @torch.no_grad()
 def __call__(	self			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = 50			,     _lowerCAmelCase = None			,     _lowerCAmelCase = "pil"			,     _lowerCAmelCase = True			,     **_lowerCAmelCase			,     )    ->							Union[Tuple, ImagePipelineOutput]:
     _lowerCAmelCase				=      self.unet.config.sample_size
     _lowerCAmelCase				=      (batch_size, 3, img_size, img_size)

     _lowerCAmelCase				=      self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _lowerCAmelCase				=      randn_tensor(_a			,     generator=_a			,     device=self.device		) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a		)

     for t in self.progress_bar(self.scheduler.timesteps		):
         # here sigma_t == t_i from the paper
         _lowerCAmelCase				=      self.scheduler.schedule[t]
         _lowerCAmelCase				=      self.scheduler.schedule[t - 1] if t > 0 else 0

         # 1. Select temporarily increased noise level sigma_hat
         # 2. Add new noise to move from sample_i to sample_hat
         _lowerCAmelCase				=      self.scheduler.add_noise_to_input(_a			,     _a			,     generator=_a		)

         # 3. Predict the noise residual given the noise magnitude `sigma_hat`
         # The model inputs and output are adjusted by following eq. (213) in [1].
         _lowerCAmelCase				=      (sigma_hat / 2) * model((sample_hat + 1) / 2			,     sigma_hat / 2		).sample

         # 4. Evaluate dx/dt at sigma_hat
         # 5. Take Euler step from sigma to sigma_prev
         _lowerCAmelCase				=      self.scheduler.step(_a			,     _a			,     _a			,     _a		)

         if sigma_prev != 0:
             # 6. Apply 2nd order correction
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _lowerCAmelCase				=      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,     sigma_prev / 2		).sample
             _lowerCAmelCase				=      self.scheduler.step_correct(
                 _a			,     _a			,     _a			,     _a			,     step_output.prev_sample			,     step_output["derivative"]			,     )
         _lowerCAmelCase				=      step_output.prev_sample

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

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_a		)






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

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


