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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  42
  lowercase_								=  42




  def __init__(   self  :						str  ,			a_  :						UNetaDModel  ,			a_  :						ScoreSdeVeScheduler					)->							List[Any]:




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




  @torch.no_grad()
  def __call__(   self  :						Optional[int]  ,			a_  :						int = 1  ,			a_  :						int = 2000  ,			a_  :						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,			a_  :						Optional[str] = "pil"  ,			a_  :						bool = True  ,			**a_  :						int  ,			)->							Union[ImagePipelineOutput, Tuple]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.unet.config.sample_size
       SCREAMING_SNAKE_CASE__					:					Tuple      =		(batch_size, 3, img_size, img_size)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.unet

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		randn_tensor(a_  ,			generator=a_					) * self.scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					int      =		sample.to(self.device					)

       self.scheduler.set_timesteps(a_					)
       self.scheduler.set_sigmas(a_					)

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

            # correction step
            for _ in range(self.scheduler.config.correct_steps					):
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.unet(a_  ,			a_					).sample
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		self.scheduler.step_correct(a_  ,			a_  ,			generator=a_					).prev_sample

            # prediction step
            SCREAMING_SNAKE_CASE__					:					Any      =		model(a_  ,			a_					).sample
            SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler.step_pred(a_  ,			a_  ,			a_  ,			generator=a_					)

            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		output.prev_sample, output.prev_sample_mean

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

       if not return_dict:
            return (sample,)

       return ImagePipelineOutput(images=a_					)




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,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """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:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """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:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """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:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """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

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


