'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 	_lowercase		(      SCREAMING_SNAKE_CASE__ ):

 def __init__(				self			:							Dict     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Any    )     ->		Tuple:

  """simple docstring"""





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


 @torch.no_grad()
 def __call__(				self			:							List[Any]     ,   snake_case			:							int = 1     ,   snake_case			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,   snake_case			:							int = 1_0_0_0     ,   snake_case			:							Optional[str] = "pil"     ,   snake_case			:							bool = True     ,   )     ->		str:

  """simple docstring"""





  if isinstance(self.unet.config.sample_size     ,   a_    ):
   UpperCamelCase_      :		Tuple				=      (
       batch_size,
       self.unet.config.in_channels,
       self.unet.config.sample_size,
       self.unet.config.sample_size,
   )
  else:
   UpperCamelCase_      :		int				=      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

  if self.device.type == "mps":
   # randn does not work reproducibly on mps
   UpperCamelCase_      :		Tuple				=      randn_tensor(a_     ,   generator=a_    )
   UpperCamelCase_      :		Optional[Any]				=      image.to(self.device    )
  else:
   UpperCamelCase_      :		Tuple				=      randn_tensor(a_     ,   generator=a_     ,   device=self.device    )

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

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

   # 2. compute previous image: x_t -> x_t-1
   UpperCamelCase_      :		List[Any]				=      self.scheduler.step(a_     ,   a_     ,   a_     ,   generator=a_    ).prev_sample

  UpperCamelCase_      :		str				=      (image / 2 + 0.5).clamp(0     ,   1    )
  UpperCamelCase_      :		Dict				=      image.cpu().permute(0     ,   2     ,   3     ,   1    ).numpy()
  if output_type == "pil":
   UpperCamelCase_      :		List[str]				=      self.numpy_to_pil(a_    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=a_    )




from typing import TYPE_CHECKING

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


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

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



