

'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    a__  (							UpperCAmelCase__  ):


   def __init__( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase = None			,				):





      """simple docstring"""





      super().__init__()
      self.register_modules(transformer=_SCREAMING_SNAKE_CASE			,				vae=_SCREAMING_SNAKE_CASE			,				scheduler=_SCREAMING_SNAKE_CASE  )

      # create a imagenet -> id dictionary for easier use
      _lowercase			:      Tuple			     =   {}
      if idalabel is not None:
         for key, value in idalabel.items():
            for label in value.split(","  ):
               _lowercase			:      Tuple			     =   int(_SCREAMING_SNAKE_CASE  )
         _lowercase			:      Optional[Any]			     =   dict(sorted(self.labels.items()  )  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      if not isinstance(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  ):
         _lowercase			:      List[Any]			     =   list(_SCREAMING_SNAKE_CASE  )

      for l in label:
         if l not in self.labels:
            raise ValueError(
                f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''  )

      return [self.labels[l] for l in label]






   @torch.no_grad()
   def __call__( self			,				_UpperCamelCase			,				_UpperCamelCase = 4.0			,				_UpperCamelCase = None			,				_UpperCamelCase = 50			,				_UpperCamelCase = "pil"			,				_UpperCamelCase = True			,				):





      """simple docstring"""





      _lowercase			:      List[str]			     =   len(_SCREAMING_SNAKE_CASE  )
      _lowercase			:      List[str]			     =   self.transformer.config.sample_size
      _lowercase			:      Optional[Any]			     =   self.transformer.config.in_channels

      _lowercase			:      List[Any]			     =   randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)			,				generator=_SCREAMING_SNAKE_CASE			,				device=self.device			,				dtype=self.transformer.dtype			,				)
      _lowercase			:      Tuple			     =   torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

      _lowercase			:      str			     =   torch.tensor(_SCREAMING_SNAKE_CASE			,				device=self.device  ).reshape(-1  )
      _lowercase			:      int			     =   torch.tensor([1000] * batch_size			,				device=self.device  )
      _lowercase			:      Optional[int]			     =   torch.cat([class_labels, class_null]			,				0  ) if guidance_scale > 1 else class_labels

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

      for t in self.progress_bar(self.scheduler.timesteps  ):
         if guidance_scale > 1:
            _lowercase			:      str			     =   latent_model_input[: len(_SCREAMING_SNAKE_CASE  ) // 2]
            _lowercase			:      Optional[Any]			     =   torch.cat([half, half]			,				dim=0  )
         _lowercase			:      Optional[int]			     =   self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  )

         _lowercase			:      Any			     =   t
         if not torch.is_tensor(_SCREAMING_SNAKE_CASE  ):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            _lowercase			:      Union[str, Any]			     =   latent_model_input.device.type == """mps"""
            if isinstance(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  ):
               _lowercase			:      Dict			     =   torch.floataa if is_mps else torch.floataa
            else:
               _lowercase			:      Dict			     =   torch.intaa if is_mps else torch.intaa
            _lowercase			:      Optional[Any]			     =   torch.tensor([timesteps]			,				dtype=_SCREAMING_SNAKE_CASE			,				device=latent_model_input.device  )
         elif len(timesteps.shape  ) == 0:
            _lowercase			:      Optional[Any]			     =   timesteps[None].to(latent_model_input.device  )
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         _lowercase			:      int			     =   timesteps.expand(latent_model_input.shape[0]  )
         # predict noise model_output
         _lowercase			:      int			     =   self.transformer(
             _SCREAMING_SNAKE_CASE			,				timestep=_SCREAMING_SNAKE_CASE			,				class_labels=_SCREAMING_SNAKE_CASE  ).sample

         # perform guidance
         if guidance_scale > 1:
            _lowercase			:      List[Any]			     =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
            _lowercase			:      Dict			     =   torch.split(_SCREAMING_SNAKE_CASE			,				len(_SCREAMING_SNAKE_CASE  ) // 2			,				dim=0  )

            _lowercase			:      List[str]			     =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
            _lowercase			:      Optional[Any]			     =   torch.cat([half_eps, half_eps]			,				dim=0  )

            _lowercase			:      Optional[Any]			     =   torch.cat([eps, rest]			,				dim=1  )

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
            _lowercase			:      Optional[int]			     =   torch.split(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				dim=1  )
         else:
            _lowercase			:      str			     =   noise_pred

         # compute previous image: x_t -> x_t-1
         _lowercase			:      Any			     =   self.scheduler.step(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  ).prev_sample

      if guidance_scale > 1:
         _lowercase			:      str			     =   latent_model_input.chunk(2			,				dim=0  )
      else:
         _lowercase			:      Optional[int]			     =   latent_model_input

      _lowercase			:      Optional[int]			     =   1 / self.vae.config.scaling_factor * latents
      _lowercase			:      Tuple			     =   self.vae.decode(_SCREAMING_SNAKE_CASE  ).sample

      _lowercase			:      Any			     =   (samples / 2 + 0.5).clamp(0			,				1  )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      _lowercase			:      Tuple			     =   samples.cpu().permute(0			,				2			,				3			,				1  ).float().numpy()

      if output_type == "pil":
         _lowercase			:      List[Any]			     =   self.numpy_to_pil(_SCREAMING_SNAKE_CASE  )

      if not return_dict:
         return (samples,)

      return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE  )











"""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__)
