


"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCAmelCase     					=   logging.get_logger(__name__)


if is_vision_available():
   import PIL

class 			lowercase_	(			UpperCAmelCase_    ):
      __magic_name__  :				Tuple 							=					['''pixel_values''']







      def __init__(   self			:				List[str]		,	_lowercase			:				bool = True		,	_lowercase			:				Dict[str, int] = None		,	_lowercase			:				PILImageResampling = PILImageResampling.BICUBIC		,	_lowercase			:				bool = True		,	_lowercase			:				Dict[str, int] = None		,	_lowercase			:				bool = True		,	_lowercase			:				Union[int, float] = 1 / 2_5_5		,	_lowercase			:				bool = True		,	_lowercase			:				Optional[Union[float, List[float]]] = None		,	_lowercase			:				Optional[Union[float, List[float]]] = None		,	_lowercase			:				bool = True		,	**_lowercase			:				List[str]		,	):
        super().__init__(**lowerCamelCase__     )
        lowerCAmelCase__     :     int										=       size if size is not None else {"""shortest_edge""": 2_2_4}
        lowerCAmelCase__     :     Tuple										=       get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
        lowerCAmelCase__     :     Any										=       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        lowerCAmelCase__     :     Tuple										=       get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__		,	param_name="crop_size"     )

        lowerCAmelCase__     :     str										=       do_resize
        lowerCAmelCase__     :     Dict										=       size
        lowerCAmelCase__     :     Dict										=       resample
        lowerCAmelCase__     :     Union[str, Any]										=       do_center_crop
        lowerCAmelCase__     :     str										=       crop_size
        lowerCAmelCase__     :     Optional[Any]										=       do_rescale
        lowerCAmelCase__     :     Any										=       rescale_factor
        lowerCAmelCase__     :     Optional[Any]										=       do_normalize
        lowerCAmelCase__     :     str										=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase__     :     int										=       image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase__     :     Dict										=       do_convert_rgb







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]		,	_lowercase			:				np.ndarray		,	_lowercase			:				Dict[str, int]		,	_lowercase			:				PILImageResampling = PILImageResampling.BICUBIC		,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None		,	**_lowercase			:				Any		,	):
        lowerCAmelCase__     :     Optional[int]										=       get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
        if "shortest_edge" not in size:
          raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"     )
        lowerCAmelCase__     :     Any										=       get_resize_output_image_size(lowerCamelCase__		,	size=size["shortest_edge"]		,	default_to_square=lowerCamelCase__     )
        return resize(lowerCamelCase__		,	size=lowerCamelCase__		,	resample=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )







      def 					_lowerCAmelCase							(   self			:				List[Any]		,	_lowercase			:				np.ndarray		,	_lowercase			:				Dict[str, int]		,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None		,	**_lowercase			:				Union[str, Any]		,	):
        lowerCAmelCase__     :     Any										=       get_size_dict(lowerCamelCase__     )
        if "height" not in size or "width" not in size:
          raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"     )
        return center_crop(lowerCamelCase__		,	size=(size["height"], size["width"])		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )







      def 					_lowerCAmelCase							(   self			:				int		,	_lowercase			:				np.ndarray		,	_lowercase			:				Union[int, float]		,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None		,	**_lowercase			:				List[str]		,	):
        return rescale(lowerCamelCase__		,	scale=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )







      def 					_lowerCAmelCase							(   self			:				Optional[int]		,	_lowercase			:				np.ndarray		,	_lowercase			:				Union[float, List[float]]		,	_lowercase			:				Union[float, List[float]]		,	_lowercase			:				Optional[Union[str, ChannelDimension]] = None		,	**_lowercase			:				Optional[Any]		,	):
        return normalize(lowerCamelCase__		,	mean=lowerCamelCase__		,	std=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )







      def 					_lowerCAmelCase							(   self			:				str		,	_lowercase			:				ImageInput		,	_lowercase			:				bool = None		,	_lowercase			:				Dict[str, int] = None		,	_lowercase			:				PILImageResampling = None		,	_lowercase			:				bool = None		,	_lowercase			:				int = None		,	_lowercase			:				bool = None		,	_lowercase			:				float = None		,	_lowercase			:				bool = None		,	_lowercase			:				Optional[Union[float, List[float]]] = None		,	_lowercase			:				Optional[Union[float, List[float]]] = None		,	_lowercase			:				bool = None		,	_lowercase			:				Optional[Union[str, TensorType]] = None		,	_lowercase			:				Optional[ChannelDimension] = ChannelDimension.FIRST		,	**_lowercase			:				Optional[Any]		,	):
        lowerCAmelCase__     :     Any										=       do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__     :     List[str]										=       size if size is not None else self.size
        lowerCAmelCase__     :     Optional[int]										=       get_size_dict(lowerCamelCase__		,	param_name="size"		,	default_to_square=lowerCamelCase__     )
        lowerCAmelCase__     :     str										=       resample if resample is not None else self.resample
        lowerCAmelCase__     :     Union[str, Any]										=       do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase__     :     List[Any]										=       crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase__     :     str										=       get_size_dict(lowerCamelCase__		,	param_name="crop_size"		,	default_to_square=lowerCamelCase__     )
        lowerCAmelCase__     :     List[str]										=       do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__     :     Union[str, Any]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__     :     Dict										=       do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__     :     Optional[Any]										=       image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__     :     Optional[Any]										=       image_std if image_std is not None else self.image_std
        lowerCAmelCase__     :     Optional[int]										=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__     :     List[Any]										=       make_list_of_images(lowerCamelCase__     )

        if not valid_images(lowerCamelCase__     ):
          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:
          raise ValueError("Size must be specified if do_resize is True."     )

        if do_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop is True."     )

        if do_rescale and rescale_factor is None:
          raise ValueError("Rescale factor must be specified if do_rescale is True."     )

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError("Image mean and std must be specified if do_normalize is True."     )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
          lowerCAmelCase__     :     Dict										=       [convert_to_rgb(lowerCamelCase__     ) for image in images]

        # All transformations expect numpy arrays.
        lowerCAmelCase__     :     Dict										=       [to_numpy_array(lowerCamelCase__     ) for image in images]

        if do_resize:
          lowerCAmelCase__     :     Optional[int]										=       [self.resize(image=lowerCamelCase__		,	size=lowerCamelCase__		,	resample=lowerCamelCase__     ) for image in images]

        if do_center_crop:
          lowerCAmelCase__     :     Optional[int]										=       [self.center_crop(image=lowerCamelCase__		,	size=lowerCamelCase__     ) for image in images]

        if do_rescale:
          lowerCAmelCase__     :     List[Any]										=       [self.rescale(image=lowerCamelCase__		,	scale=lowerCamelCase__     ) for image in images]

        if do_normalize:
          lowerCAmelCase__     :     Optional[Any]										=       [self.normalize(image=lowerCamelCase__		,	mean=lowerCamelCase__		,	std=lowerCamelCase__     ) for image in images]

        lowerCAmelCase__     :     List[str]										=       [to_channel_dimension_format(lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

        lowerCAmelCase__     :     Optional[int]										=       {"""pixel_values""": images}
        return BatchFeature(data=lowerCamelCase__		,	tensor_type=lowerCamelCase__     )








import re

from filelock import FileLock


try:
       import nltk

       UpperCamelCase    							=      True
except (ImportError, ModuleNotFoundError):
       UpperCamelCase    							=      False

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  str:
   re.sub("""<n>"""					,							""""""					,							__lowerCAmelCase							)  # 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(__lowerCAmelCase							)							)



