
"""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


UpperCAmelCase__   :					Union[str, Any]												=				logging.get_logger(__name__)

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     b.T
							SCREAMING_SNAKE_CASE__		: Any     =     np.sum(np.square(_snake_case		)     ,axis=1		)
							SCREAMING_SNAKE_CASE__		: int     =     np.sum(np.square(_snake_case		)     ,axis=0		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     np.matmul(_snake_case     ,_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     aa[:, None] - 2 * ab + ba[None, :]
							return d

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     x.reshape(-1     ,3		)
							SCREAMING_SNAKE_CASE__		: Dict     =     squared_euclidean_distance(_snake_case     ,_snake_case		)
							return np.argmin(_snake_case     ,axis=1		)



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[int]       =     ['''pixel_values''']



				def __init__(self     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = True     , **SCREAMING_SNAKE_CASE__     , )      ->	None:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     size if size is not None else {"""height""": 2_56, """width""": 2_56}
											SCREAMING_SNAKE_CASE__		: int     =     get_size_dict(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: int     =     np.array(SCREAMING_SNAKE_CASE__					) if clusters is not None else None
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     do_resize
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     resample
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_normalize
											SCREAMING_SNAKE_CASE__		: Dict     =     do_color_quantize



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     get_size_dict(SCREAMING_SNAKE_CASE__					)
											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(
											    SCREAMING_SNAKE_CASE__     , size=(size["""height"""], size["""width"""])     , resample=SCREAMING_SNAKE_CASE__     , data_format=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , )      ->	np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     rescale(image=SCREAMING_SNAKE_CASE__     , scale=1 / 127.5     , data_format=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image - 1
											return image



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST     , **SCREAMING_SNAKE_CASE__     , )      ->	PIL.Image.Image:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE__		: str     =     size if size is not None else self.size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     get_size_dict(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE__		: List[str]     =     do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE__		: Tuple     =     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
											SCREAMING_SNAKE_CASE__		: List[Any]     =     clusters if clusters is not None else self.clusters
											SCREAMING_SNAKE_CASE__		: List[Any]     =     np.array(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     make_list_of_images(SCREAMING_SNAKE_CASE__					)

											if not valid_images(SCREAMING_SNAKE_CASE__					):
																		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.
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [to_numpy_array(SCREAMING_SNAKE_CASE__					) for image in images]

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

											if do_normalize:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [self.normalize(image=SCREAMING_SNAKE_CASE__					) for image in images]

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

																		# flatten to (batch_size, height*width)
																		SCREAMING_SNAKE_CASE__		: Any     =     images.shape[0]
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     images.reshape(SCREAMING_SNAKE_CASE__     , -1					)

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

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""input_ids""": images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE__     , tensor_type=SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




