





"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					42
   __UpperCAmelCase        =					42



   def __init__(       self      ,   snake_case      ,   snake_case	) ->    Union[str, Any]:







      """simple docstring"""
      super().__init__()

      lowercase    :			int  =       PNDMScheduler.from_config(scheduler.config	)

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





   @torch.no_grad()
   def __call__(       self      ,   snake_case = 1      ,   snake_case = 5_0      ,   snake_case = None      ,   snake_case = "pil"      ,   snake_case = True      ,   **snake_case      ,   ) ->    Union[ImagePipelineOutput, Tuple]:







      """simple docstring"""
      lowercase    :			List[str]  =       randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,   generator=snake_case      ,   device=self.device      ,   )

      self.scheduler.set_timesteps(snake_case	)
      for t in self.progress_bar(self.scheduler.timesteps	):
         lowercase    :			Optional[Any]  =       self.unet(snake_case      ,   snake_case	).sample

         lowercase    :			Optional[int]  =       self.scheduler.step(snake_case      ,   snake_case      ,   snake_case	).prev_sample

      lowercase    :			Union[str, Any]  =       (image / 2 + 0.5).clamp(0      ,   1	)
      lowercase    :			str  =       image.cpu().permute(0      ,   2      ,   3      ,   1	).numpy()
      if output_type == "pil":
         lowercase    :			Union[str, Any]  =       self.numpy_to_pil(snake_case	)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=snake_case	)









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

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


lowerCAmelCase:	Union[str, Any]							      ={
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Dict							      =["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Optional[Any]							      =[
       "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaV2ForMaskedLM",
       "TFDebertaV2ForQuestionAnswering",
       "TFDebertaV2ForSequenceClassification",
       "TFDebertaV2ForTokenClassification",
       "TFDebertaV2Model",
       "TFDebertaV2PreTrainedModel",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	str							      =[
       "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaV2ForMaskedLM",
       "DebertaV2ForMultipleChoice",
       "DebertaV2ForQuestionAnswering",
       "DebertaV2ForSequenceClassification",
       "DebertaV2ForTokenClassification",
       "DebertaV2Model",
       "DebertaV2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

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


