


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
A			:						Union[str, Any]        =  0B101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
A			:						Any        =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]



class 				A       :
					'''simple docstring'''






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




							"""simple docstring"""


							lowercase__	     =						WATERMARK_BITS
							lowercase__	     =						WatermarkEncoder()

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


					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		torch.FloatTensor						)		->  Optional[Any]:




							"""simple docstring"""


							if images.shape[-1] < 256:
									return images

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

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

							lowercase__	     =						torch.from_numpy(np.array(_UpperCAmelCase						)						).permute(0 ,   3 ,   1 ,   2						)

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




'''simple docstring'''



import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 				_snake_case					(   datasets.BuilderConfig			):
  SCREAMING_SNAKE_CASE			:       Optional[datasets.Features]				=     None



class 				_snake_case					(   datasets.ArrowBasedBuilder			):
  SCREAMING_SNAKE_CASE			:       Tuple				=     PandasConfig

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return datasets.DatasetInfo(features=self.config.features	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if not self.config.data_files:
          raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'	)
      lowerCAmelCase 						=							dl_manager.download_and_extract(self.config.data_files	)
      if isinstance(_SCREAMING_SNAKE_CASE      , (str, list, tuple)	):
          lowerCAmelCase 						=							data_files
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      , gen_kwargs={'files': files}	)]
      lowerCAmelCase 						=							[]
      for split_name, files in data_files.items():
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      , gen_kwargs={'files': files}	)	)
      return splits

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if self.config.features is not None:
          # more expensive cast to support nested features with keys in a different order
          # allows str <-> int/float or str to Audio for example
          lowerCAmelCase 						=							table_cast(_SCREAMING_SNAKE_CASE      , self.config.features.arrow_schema	)
      return pa_table


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      for i, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE	)	):
          with open(_SCREAMING_SNAKE_CASE      , 'rb'	) as f:
              lowerCAmelCase 						=							pa.Table.from_pandas(pd.read_pickle(_SCREAMING_SNAKE_CASE	)	)
              yield i, self._cast_table(_SCREAMING_SNAKE_CASE	)





