


"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							UNetaDModel
    UpperCAmelCase       :							ScoreSdeVeScheduler






    def __init__(					self				:    Optional[int]				, __UpperCAmelCase				:    UNetaDModel				, __UpperCAmelCase				:    ScoreSdeVeScheduler):
      super().__init__()
      self.register_modules(unet=__UpperCAmelCase				, scheduler=__UpperCAmelCase)






    @torch.no_grad()
    def __call__(					self				:    Dict				, __UpperCAmelCase				:    int = 1				, __UpperCAmelCase				:    int = 2000				, __UpperCAmelCase				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				, __UpperCAmelCase				:    Optional[str] = "pil"				, __UpperCAmelCase				:    bool = True				, **__UpperCAmelCase				:    Optional[int]				, ):
      a						:		int      							=			self.unet.config.sample_size
      a						:		List[Any]      							=			(batch_size, 3, img_size, img_size)

      a						:		str      							=			self.unet

      a						:		Tuple      							=			randn_tensor(__UpperCAmelCase				, generator=__UpperCAmelCase) * self.scheduler.init_noise_sigma
      a						:		List[Any]      							=			sample.to(self.device)

      self.scheduler.set_timesteps(__UpperCAmelCase)
      self.scheduler.set_sigmas(__UpperCAmelCase)

      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
        a						:		Optional[Any]      							=			self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device)

        # correction step
        for _ in range(self.scheduler.config.correct_steps):
          a						:		str      							=			self.unet(__UpperCAmelCase				, __UpperCAmelCase).sample
          a						:		Tuple      							=			self.scheduler.step_correct(__UpperCAmelCase				, __UpperCAmelCase				, generator=__UpperCAmelCase).prev_sample

        # prediction step
        a						:		Optional[int]      							=			model(__UpperCAmelCase				, __UpperCAmelCase).sample
        a						:		int      							=			self.scheduler.step_pred(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, generator=__UpperCAmelCase)

        a							,		a						:		List[Any]      							=			output.prev_sample, output.prev_sample_mean

      a						:		int      							=			sample_mean.clamp(0				, 1)
      a						:		Any      							=			sample.cpu().permute(0				, 2				, 3				, 1).numpy()
      if output_type == "pil":
        a						:		str      							=			self.numpy_to_pil(__UpperCAmelCase)

      if not return_dict:
        return (sample,)

      return ImagePipelineOutput(images=__UpperCAmelCase)










"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    					=      {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      ["""BertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BertForMaskedLM""",
     """BertForMultipleChoice""",
     """BertForNextSentencePrediction""",
     """BertForPreTraining""",
     """BertForQuestionAnswering""",
     """BertForSequenceClassification""",
     """BertForTokenClassification""",
     """BertLayer""",
     """BertLMHeadModel""",
     """BertModel""",
     """BertPreTrainedModel""",
     """load_tf_weights_in_bert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFBertEmbeddings""",
     """TFBertForMaskedLM""",
     """TFBertForMultipleChoice""",
     """TFBertForNextSentencePrediction""",
     """TFBertForPreTraining""",
     """TFBertForQuestionAnswering""",
     """TFBertForSequenceClassification""",
     """TFBertForTokenClassification""",
     """TFBertLMHeadModel""",
     """TFBertMainLayer""",
     """TFBertModel""",
     """TFBertPreTrainedModel""",
 ]
try:
 if not is_tensorflow_text_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      ["""TFBertTokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """FlaxBertForCausalLM""",
     """FlaxBertForMaskedLM""",
     """FlaxBertForMultipleChoice""",
     """FlaxBertForNextSentencePrediction""",
     """FlaxBertForPreTraining""",
     """FlaxBertForQuestionAnswering""",
     """FlaxBertForSequenceClassification""",
     """FlaxBertForTokenClassification""",
     """FlaxBertModel""",
     """FlaxBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
 from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_fast import BertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert import (
      BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertForMaskedLM,
      BertForMultipleChoice,
      BertForNextSentencePrediction,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertForTokenClassification,
      BertLayer,
      BertLMHeadModel,
      BertModel,
      BertPreTrainedModel,
      load_tf_weights_in_bert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bert import (
      TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFBertEmbeddings,
      TFBertForMaskedLM,
      TFBertForMultipleChoice,
      TFBertForNextSentencePrediction,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertForTokenClassification,
      TFBertLMHeadModel,
      TFBertMainLayer,
      TFBertModel,
      TFBertPreTrainedModel,
  )

 try:
  if not is_tensorflow_text_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_tf import TFBertTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bert import (
      FlaxBertForCausalLM,
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
      FlaxBertPreTrainedModel,
  )

else:
 import sys

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






