





import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase_					:   Union[str, Any] 							=						0b101_100_111_110_110_010_010_000_011_110_111_011_000_110_011_110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCAmelCase_					:   Union[str, Any] 							=						[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class      lowercase__    :







  def __init__(	self						):
       lowerCAmelCase_					:       Optional[int]    =		WATERMARK_BITS
       lowerCAmelCase_					:       Dict    =		WatermarkEncoder()

       self.encoder.set_watermark("""bits"""    ,						self.watermark						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # can't encode images that are smaller than 256
       if images.shape[-1] < 256:
            return images

       lowerCAmelCase_					:       Any    =		(255 * (images / 2 + 0.5)).cpu().permute(0    ,						2    ,						3    ,						1						).float().numpy()

       lowerCAmelCase_					:       int    =		[self.encoder.encode(_UpperCAmelCase    ,						"""dwtDct"""						) for image in images]

       lowerCAmelCase_					:       Any    =		torch.from_numpy(np.array(_UpperCAmelCase						)						).permute(0    ,						3    ,						1    ,						2						)

       lowerCAmelCase_					:       Dict    =		torch.clamp(2 * (images / 255 - 0.5)    ,						min=-1.0    ,						max=1.0						)
       return images







from typing import TYPE_CHECKING

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


UpperCAmelCase_					:   int 							=						{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Optional[int] 							=						["""PLBartTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PLBartForCausalLM""",
          """PLBartForConditionalGeneration""",
          """PLBartForSequenceClassification""",
          """PLBartModel""",
          """PLBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_plbart import PLBartTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_plbart import (
                PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                PLBartForCausalLM,
                PLBartForConditionalGeneration,
                PLBartForSequenceClassification,
                PLBartModel,
                PLBartPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   List[str] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
