






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							Union[int, Iterable[int]]     ,       __snake_case    :							bool     ,       __snake_case    :							int  ):


					'''simple docstring'''


					def constraint_to_multiple_of(__snake_case    :							List[str]     ,       __snake_case    :							Dict     ,       __snake_case    :							Any=0     ,       __snake_case    :							int=None  ):
										UpperCAmelCase_    :      Dict            =						round(val / multiple  ) * multiple

										if max_val is not None and x > max_val:
															UpperCAmelCase_    :      Any            =						math.floor(val / multiple  ) * multiple

										if x < min_val:
															UpperCAmelCase_    :      Dict            =						math.ceil(val / multiple  ) * multiple

										return x

					UpperCAmelCase_    :      Union[str, Any]            =						(output_size, output_size) if isinstance(__a     ,       __a  ) else output_size

					UpperCAmelCase_    :      List[Any]            =						get_image_size(__a  )
					UpperCAmelCase_    :      Any            =						output_size

					# determine new height and width
					UpperCAmelCase_    :      Union[str, Any]            =						output_height / input_height
					UpperCAmelCase_    :      Tuple            =						output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width  ) < abs(1 - scale_height  ):
															# fit width
															UpperCAmelCase_    :      Optional[Any]            =						scale_width
										else:
															# fit height
															UpperCAmelCase_    :      Tuple            =						scale_height

					UpperCAmelCase_    :      Optional[Any]            =						constraint_to_multiple_of(scale_height * input_height     ,       multiple=__a  )
					UpperCAmelCase_    :      int            =						constraint_to_multiple_of(scale_width * input_width     ,       multiple=__a  )

					return (new_height, new_width)






class   lowerCamelCase      (__lowercase			):







		'''simple docstring'''



		_snake_case    :    Optional[Any]       						=  ["pixel_values"]



		def __init__(							self				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = PILImageResampling.BILINEAR				,		_UpperCamelCase = False				,		_UpperCamelCase = 1				,		_UpperCamelCase = True				,		_UpperCamelCase = 1 / 2_5_5				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**_a   )
							UpperCAmelCase_    :      Optional[int]            =						size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
							UpperCAmelCase_    :      Optional[Any]            =						get_size_dict(_a   )
							UpperCAmelCase_    :      Any            =						do_resize
							UpperCAmelCase_    :      Dict            =						size
							UpperCAmelCase_    :      str            =						keep_aspect_ratio
							UpperCAmelCase_    :      Any            =						ensure_multiple_of
							UpperCAmelCase_    :      Optional[Any]            =						resample
							UpperCAmelCase_    :      List[Any]            =						do_rescale
							UpperCAmelCase_    :      int            =						rescale_factor
							UpperCAmelCase_    :      Any            =						do_normalize
							UpperCAmelCase_    :      Union[str, Any]            =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCAmelCase_    :      List[str]            =						image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = False				,		_UpperCamelCase = 1				,		_UpperCamelCase = PILImageResampling.BICUBIC				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							UpperCAmelCase_    :      str            =						get_size_dict(_a   )
							if "height" not in size or "width" not in size:
												raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"   )
							UpperCAmelCase_    :      Optional[Any]            =						get_resize_output_image_size(
							    _a				,		output_size=(size['height'], size['width'])				,		keep_aspect_ratio=_a				,		multiple=_a				,		)
							return resize(_a				,		size=_a				,		resample=_a				,		data_format=_a				,		**_a   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							int:
							return rescale(_a				,		scale=_a				,		data_format=_a				,		**_a   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							return normalize(_a				,		mean=_a				,		std=_a				,		data_format=_a				,		**_a   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = ChannelDimension.FIRST				,		**_UpperCamelCase				,		)	->							PIL.Image.Image:
							UpperCAmelCase_    :      Optional[int]            =						do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_    :      Union[str, Any]            =						size if size is not None else self.size
							UpperCAmelCase_    :      str            =						get_size_dict(_a   )
							UpperCAmelCase_    :      List[Any]            =						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							UpperCAmelCase_    :      Optional[Any]            =						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							UpperCAmelCase_    :      str            =						resample if resample is not None else self.resample
							UpperCAmelCase_    :      str            =						do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_    :      Union[str, Any]            =						rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_    :      List[Any]            =						do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_    :      str            =						image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_    :      Tuple            =						image_std if image_std is not None else self.image_std

							UpperCAmelCase_    :      Dict            =						make_list_of_images(_a   )

							if not valid_images(_a   ):
												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_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.'   )

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

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

							if do_rescale:
												UpperCAmelCase_    :      Optional[Any]            =						[self.rescale(image=_a				,		scale=_a   ) for image in images]

							if do_normalize:
												UpperCAmelCase_    :      Optional[int]            =						[self.normalize(image=_a				,		mean=_a				,		std=_a   ) for image in images]

							UpperCAmelCase_    :      int            =						[to_channel_dimension_format(_a				,		_a   ) for image in images]

							UpperCAmelCase_    :      Tuple            =						{'''pixel_values''': images}
							return BatchFeature(data=_a				,		tensor_type=_a   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							Any:
							UpperCAmelCase_    :      Optional[Any]            =						outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
												if len(_a   ) != len(_a   ):
																	raise ValueError(
																	    'Make sure that you pass in as many target sizes as the batch dimension of the logits'   )

												if is_torch_tensor(_a   ):
																	UpperCAmelCase_    :      List[Any]            =						target_sizes.numpy()

												UpperCAmelCase_    :      str            =						[]

												for idx in range(len(_a   )   ):
																	UpperCAmelCase_    :      str            =						torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0   )				,		size=target_sizes[idx]				,		mode='bilinear'				,		align_corners=_a   )
																	UpperCAmelCase_    :      Union[str, Any]            =						resized_logits[0].argmax(dim=0   )
																	semantic_segmentation.append(_a   )
							else:
												UpperCAmelCase_    :      Tuple            =						logits.argmax(dim=1   )
												UpperCAmelCase_    :      Union[str, Any]            =						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

							return semantic_segmentation









import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



