



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
  import PIL


__a:			Optional[Any]	       =       logging.get_logger(__name__)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= b.T
 lowercase__    :   Optional[Any]						= np.sum(np.square(UpperCAmelCase					)		,					axis=1					)
 lowercase__    :   int						= np.sum(np.square(UpperCAmelCase					)		,					axis=0					)
 lowercase__    :   List[Any]						= np.matmul(UpperCAmelCase		,					UpperCAmelCase					)
 lowercase__    :   Any						= aa[:, None] - 2 * ab + ba[None, :]
 return d
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[Any]						= x.reshape(-1		,					3					)
 lowercase__    :   Optional[int]						= squared_euclidean_distance(UpperCAmelCase		,					UpperCAmelCase					)
 return np.argmin(UpperCAmelCase		,					axis=1					)





class       UpperCAmelCase  (						UpperCamelCase__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ['''pixel_values''']

       def __init__(						self     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = PILImageResampling.BILINEAR     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = True     ,       **__lowerCAmelCase     ,       )  ->  None:
        super().__init__(**UpperCamelCase_			)
        lowercase__    :   Tuple						= size if size is not None else {'''height''': 256, '''width''': 256}
        lowercase__    :   Optional[Any]						= get_size_dict(UpperCamelCase_			)
        lowercase__    :   Any						= np.array(UpperCamelCase_			) if clusters is not None else None
        lowercase__    :   List[str]						= do_resize
        lowercase__    :   Optional[int]						= size
        lowercase__    :   str						= resample
        lowercase__    :   Any						= do_normalize
        lowercase__    :   Dict						= do_color_quantize

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = PILImageResampling.BILINEAR     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  np.ndarray:
        lowercase__    :   int						= get_size_dict(UpperCamelCase_			)
        if "height" not in size or "width" not in size:
         raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""			)
        return resize(
            UpperCamelCase_     ,       size=(size['''height'''], size['''width'''])     ,       resample=UpperCamelCase_     ,       data_format=UpperCamelCase_     ,       **UpperCamelCase_			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       )  ->  np.ndarray:
        lowercase__    :   Dict						= rescale(image=UpperCamelCase_     ,       scale=1 / 1_2_7.5     ,       data_format=UpperCamelCase_			)
        lowercase__    :   List[Any]						= image - 1
        return image


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = ChannelDimension.FIRST     ,       **__lowerCAmelCase     ,       )  ->  PIL.Image.Image:
        lowercase__    :   Optional[Any]						= do_resize if do_resize is not None else self.do_resize
        lowercase__    :   List[Any]						= size if size is not None else self.size
        lowercase__    :   int						= get_size_dict(UpperCamelCase_			)
        lowercase__    :   Optional[int]						= resample if resample is not None else self.resample
        lowercase__    :   List[str]						= do_normalize if do_normalize is not None else self.do_normalize
        lowercase__    :   Optional[int]						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        lowercase__    :   Union[str, Any]						= clusters if clusters is not None else self.clusters
        lowercase__    :   Optional[Any]						= np.array(UpperCamelCase_			)

        lowercase__    :   Dict						= make_list_of_images(UpperCamelCase_			)

        if not valid_images(UpperCamelCase_			):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

        if do_resize and size is None or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

        if do_color_quantize and clusters is None:
         raise ValueError('''Clusters must be specified if do_color_quantize is True.'''			)

        # All transformations expect numpy arrays.
        lowercase__    :   List[str]						= [to_numpy_array(UpperCamelCase_			) for image in images]

        if do_resize:
         lowercase__    :   int						= [self.resize(image=UpperCamelCase_     ,       size=UpperCamelCase_     ,       resample=UpperCamelCase_			) for image in images]

        if do_normalize:
         lowercase__    :   List[str]						= [self.normalize(image=UpperCamelCase_			) for image in images]

        if do_color_quantize:
         lowercase__    :   Dict						= [to_channel_dimension_format(UpperCamelCase_     ,       ChannelDimension.LAST			) for image in images]
         # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
         lowercase__    :   Union[str, Any]						= np.array(UpperCamelCase_			)
         lowercase__    :   Optional[Any]						= color_quantize(UpperCamelCase_     ,       UpperCamelCase_			).reshape(images.shape[:-1]			)

         # flatten to (batch_size, height*width)
         lowercase__    :   Dict						= images.shape[0]
         lowercase__    :   Union[str, Any]						= images.reshape(UpperCamelCase_     ,       -1			)

         # We need to convert back to a list of images to keep consistent behaviour across processors.
         lowercase__    :   str						= list(UpperCamelCase_			)
        else:
         lowercase__    :   int						= [to_channel_dimension_format(UpperCamelCase_     ,       UpperCamelCase_			) for image in images]

        lowercase__    :   int						= {'''input_ids''': images}
        return BatchFeature(data=UpperCamelCase_     ,       tensor_type=UpperCamelCase_			)


'''simple docstring'''

import re

from filelock import FileLock


try:
  import nltk

  __a:			Tuple	       =       True
except (ImportError, ModuleNotFoundError):
  __a:			List[Any]	       =       False

if NLTK_AVAILABLE:
  with FileLock(""".lock""") as lock:
    nltk.download("""punkt""", quiet=True)
def       __UpperCamelCase	(       UpperCAmelCase					):
 re.sub('''<n>'''		,					''''''		,					UpperCAmelCase					)  # 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(UpperCAmelCase					)					)

