

"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

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







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			Dict					,A		:			Dict					,A		:			str    ):






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

   # make sure scheduler can always be converted to DDIM
   UpperCAmelCase__					:  List[str]					   =					DDIMScheduler.from_config(scheduler.config    )

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




  @torch.no_grad()
  def __call__(  self		:			str					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			float = 0.0					,A		:			int = 50					,A		:			Optional[bool] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size					,A    ):
    UpperCAmelCase__					:  Dict					   =					(
        batch_size,
        self.unet.config.in_channels,
        self.unet.config.sample_size,
        self.unet.config.sample_size,
    )
   else:
    UpperCAmelCase__					:  Optional[int]					   =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

   if isinstance(A					,A    ) and len(A    ) != batch_size:
    raise ValueError(
        f"You have passed a list of generators of length {len(A    )}, but requested an effective batch"
        f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

   UpperCAmelCase__					:  List[str]					   =					randn_tensor(A					,generator=A					,device=self.device					,dtype=self.unet.dtype    )

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

   for t in self.progress_bar(self.scheduler.timesteps    ):
    # 1. predict noise model_output
    UpperCAmelCase__					:  List[Any]					   =					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
    UpperCAmelCase__					:  Optional[int]					   =					self.scheduler.step(
        A					,A					,A					,eta=A					,use_clipped_model_output=A					,generator=A    ).prev_sample

   UpperCAmelCase__					:  Union[str, Any]					   =					(image / 2 + 0.5).clamp(0					,1    )
   UpperCAmelCase__					:  Union[str, Any]					   =					image.cpu().permute(0					,2					,3					,1    ).numpy()
   if output_type == "pil":
    UpperCAmelCase__					:  Any					   =					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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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





