'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_A			:      Dict					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					["""pixel_values"""]
 def __init__(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	int = 32      ,       SCREAMING_SNAKE_CASE__   :	int=PILImageResampling.BILINEAR      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]      ,       )  ->    None:
  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						do_rescale
  __lowerCAmelCase											=						size_divisor
  __lowerCAmelCase											=						resample
  super().__init__(**SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[ChannelDimension] = None      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    np.ndarray:
  __lowerCAmelCase							,       __lowerCAmelCase											=						get_image_size(SCREAMING_SNAKE_CASE__							)
  # Rounds the height and width down to the closest multiple of size_divisor
  __lowerCAmelCase											=						height // size_divisor * size_divisor
  __lowerCAmelCase											=						width // size_divisor * size_divisor
  __lowerCAmelCase											=						resize(SCREAMING_SNAKE_CASE__      ,       (new_h, new_w)      ,       resample=SCREAMING_SNAKE_CASE__      ,       data_format=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
  return image
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	float      ,       SCREAMING_SNAKE_CASE__   :	Optional[ChannelDimension] = None      ,       **SCREAMING_SNAKE_CASE__   :	str							)  ->    np.ndarray:
  return rescale(image=SCREAMING_SNAKE_CASE__      ,       scale=SCREAMING_SNAKE_CASE__      ,       data_format=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[TensorType, str]] = None      ,       SCREAMING_SNAKE_CASE__   :	ChannelDimension = ChannelDimension.FIRST      ,       **SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    BatchFeature:
  __lowerCAmelCase											=						do_resize if do_resize is not None else self.do_resize
  __lowerCAmelCase											=						do_rescale if do_rescale is not None else self.do_rescale
  __lowerCAmelCase											=						size_divisor if size_divisor is not None else self.size_divisor
  __lowerCAmelCase											=						resample if resample is not None else self.resample

  if do_resize and size_divisor is None:
   raise ValueError("""size_divisor is required for resizing"""							)

  __lowerCAmelCase											=						make_list_of_images(SCREAMING_SNAKE_CASE__							)

  if not valid_images(SCREAMING_SNAKE_CASE__							):
   raise ValueError("""Invalid image(s)"""							)

  # All transformations expect numpy arrays.
  __lowerCAmelCase											=						[to_numpy_array(SCREAMING_SNAKE_CASE__							) for img in images]

  if do_resize:
   __lowerCAmelCase											=						[self.resize(SCREAMING_SNAKE_CASE__      ,       size_divisor=SCREAMING_SNAKE_CASE__      ,       resample=SCREAMING_SNAKE_CASE__							) for image in images]

  if do_rescale:
   __lowerCAmelCase											=						[self.rescale(SCREAMING_SNAKE_CASE__      ,       scale=1 / 2_55							) for image in images]

  __lowerCAmelCase											=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) for image in images]

  __lowerCAmelCase											=						{"""pixel_values""": images}
  return BatchFeature(data=SCREAMING_SNAKE_CASE__      ,       tensor_type=SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case_      )      )






