'''simple docstring'''




from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION




class       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase	):





       '''simple docstring'''



       def __init__( self					)						->	Any:
           super().__init__()

           self.register_buffer('''watermark_image'''       ,	torch.zeros((62, 62, 4)					)					)
           A_      =  None

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	Dict:
           # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

           A_      =  images[0].height
           A_      =  images[0].width

           A_      =  sample_size or h

           A_      =  min(h / sample_size       ,	w / sample_size					)
           A_  ,A_      =  (int(h / coef					), int(w / coef					)) if coef < 1 else (h, w)

           A_  ,A_      =  1024**2, img_w * img_h
           A_      =  (Sa / Sa) ** 0.5
           A_  ,A_  ,A_      =  int(K * 62					), img_w - int(14 * K					), img_h - int(14 * K					)

           if self.watermark_image_as_pil is None:
               A_      =  self.watermark_image.to(torch.uinta					).cpu().numpy()
               A_      =  Image.fromarray(_SCREAMING_SNAKE_CASE       ,	mode='''RGBA'''					)
               A_      =  watermark_image

           A_      =  self.watermark_image_as_pil.resize(
               (wm_size, wm_size)       ,	PIL_INTERPOLATION['''bicubic''']       ,	reducing_gap=_SCREAMING_SNAKE_CASE					)

           for pil_img in images:
               pil_img.paste(_SCREAMING_SNAKE_CASE       ,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)       ,	mask=wm_img.split()[-1]					)

           return images







'''simple docstring'''




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case  : Dict		       =    500_000

__snake_case       ,       __snake_case  : List[str]		       =    os.path.split(__file__)
__snake_case  : Optional[Any]		       =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Union[str, Any]:
    A_      =  dataset.select(range(0,  len(_UpperCamelCase  ),  2  )  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.sort('''numbers'''  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.shuffle()





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Optional[Any]:
    A_      =  dataset.train_test_split(0.1  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset,  _UpperCamelCase       :      Optional[int]=10  )      -> Dict:
    for shard_id in range(_UpperCamelCase  ):
        A_      =  dataset.shard(_UpperCamelCase,  _UpperCamelCase  )





def 	_UpperCAmelCase       (				)      -> Any:
    A_      =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
    A_      =  (select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''  )
        A_      =  datasets.Features({'''text''': datasets.Value('''string'''  ), '''numbers''': datasets.Value('''float32'''  )}  )
        A_      =  generate_example_dataset(
            os.path.join(_UpperCamelCase,  '''dataset.arrow'''  ),  _UpperCamelCase,  num_examples=_UpperCamelCase  )
        print('''Functions'''  )
        for func in functions:
            print(func.__name__  )
            A_      =  func(_UpperCamelCase  )

    with open(_UpperCamelCase,  '''wb'''  ) as f:
        f.write(json.dumps(_UpperCamelCase  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_indices_mapping()






