"""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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Any			):
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCAmelCase__             =							DDIMScheduler.from_config(scheduler.config			)

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







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

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size    ,lowerCamelCase__			):
													UpperCAmelCase__             =							(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													UpperCAmelCase__             =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

									UpperCAmelCase__             =							randn_tensor(lowerCamelCase__    ,generator=lowerCamelCase__    ,device=self.device    ,dtype=self.unet.dtype			)

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

									for t in self.progress_bar(self.scheduler.timesteps			):
													# 1. predict noise model_output
													UpperCAmelCase__             =							self.unet(lowerCamelCase__    ,lowerCamelCase__			).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__             =							self.scheduler.step(
													    lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,eta=lowerCamelCase__    ,use_clipped_model_output=lowerCamelCase__    ,generator=lowerCamelCase__			).prev_sample

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

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__			)








"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
