






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

import numpy as np

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


if is_torch_available():
	import torch


a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



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



				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Optional[Dict[str, int]] = None			,		UpperCamelCase__    :    PILImageResampling = PILImageResampling.BILINEAR			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Union[int, float] = 1 / 2_5_5			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		**UpperCamelCase__    :    Optional[Any]			,		):






											'''simple docstring'''







											super().__init__(**UpperCamelCase__)
											snake_case__	  =       size if size is not None else {"""shortest_edge""": 2_5_6}
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											snake_case__	  =       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		param_name="""crop_size""")
											snake_case__	  =       do_resize
											snake_case__	  =       size
											snake_case__	  =       resample
											snake_case__	  =       do_center_crop
											snake_case__	  =       crop_size
											snake_case__	  =       do_rescale
											snake_case__	  =       rescale_factor
											snake_case__	  =       do_normalize
											snake_case__	  =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											snake_case__	  =       image_std if image_std is not None else IMAGENET_STANDARD_STD



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Dict[str, int]			,		UpperCamelCase__    :    PILImageResampling = PILImageResampling.BICUBIC			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







											snake_case__	  =       get_size_dict(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											if "shortest_edge" not in size:
																		raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
											snake_case__	  =       get_resize_output_image_size(UpperCamelCase__			,		size=size["""shortest_edge"""]			,		default_to_square=UpperCamelCase__)
											return resize(UpperCamelCase__			,		size=UpperCamelCase__			,		resample=UpperCamelCase__			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Dict[str, int]			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    Optional[int]			,		):






											'''simple docstring'''







											snake_case__	  =       get_size_dict(UpperCamelCase__)
											if "height" not in size or "width" not in size:
																		raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''')
											return center_crop(UpperCamelCase__			,		size=(size["""height"""], size["""width"""])			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    float			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    List[Any]):






											'''simple docstring'''







											return rescale(UpperCamelCase__			,		scale=UpperCamelCase__			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    np.ndarray			,		UpperCamelCase__    :    Union[float, List[float]]			,		UpperCamelCase__    :    Union[float, List[float]]			,		UpperCamelCase__    :    Optional[Union[str, ChannelDimension]] = None			,		**UpperCamelCase__    :    Tuple			,		):






											'''simple docstring'''







											return normalize(UpperCamelCase__			,		mean=UpperCamelCase__			,		std=UpperCamelCase__			,		data_format=UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    ImageInput			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    PILImageResampling = None			,		UpperCamelCase__    :    bool = None			,		UpperCamelCase__    :    Dict[str, int] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[float] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[float, List[float]]] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		UpperCamelCase__    :    Union[str, ChannelDimension] = ChannelDimension.FIRST			,		**UpperCamelCase__    :    Tuple			,		):






											'''simple docstring'''







											snake_case__	  =       do_resize if do_resize is not None else self.do_resize
											snake_case__	  =       size if size is not None else self.size
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		default_to_square=UpperCamelCase__)
											snake_case__	  =       resample if resample is not None else self.resample
											snake_case__	  =       do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case__	  =       crop_size if crop_size is not None else self.crop_size
											snake_case__	  =       get_size_dict(UpperCamelCase__			,		param_name="""crop_size""")
											snake_case__	  =       do_rescale if do_rescale is not None else self.do_rescale
											snake_case__	  =       rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case__	  =       do_normalize if do_normalize is not None else self.do_normalize
											snake_case__	  =       image_mean if image_mean is not None else self.image_mean
											snake_case__	  =       image_std if image_std is not None else self.image_std

											snake_case__	  =       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:
																		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.""")

											# All transformations expect numpy arrays.
											snake_case__	  =       [to_numpy_array(UpperCamelCase__) for image in images]

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

											if do_center_crop:
																		snake_case__	  =       [self.center_crop(image=UpperCamelCase__			,		size=UpperCamelCase__) for image in images]

											if do_rescale:
																		snake_case__	  =       [self.rescale(image=UpperCamelCase__			,		scale=UpperCamelCase__) for image in images]

											if do_normalize:
																		snake_case__	  =       [self.normalize(image=UpperCamelCase__			,		mean=UpperCamelCase__			,		std=UpperCamelCase__) for image in images]

											snake_case__	  =       [to_channel_dimension_format(UpperCamelCase__			,		UpperCamelCase__) for image in images]

											snake_case__	  =       {"""pixel_values""": images}
											return BatchFeature(data=UpperCamelCase__			,		tensor_type=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    List[Tuple] = None):






											'''simple docstring'''







											snake_case__	  =       outputs.logits

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

																		if is_torch_tensor(UpperCamelCase__):
																									snake_case__	  =       target_sizes.numpy()

																		snake_case__	  =       []

																		for idx in range(len(UpperCamelCase__)):
																									snake_case__	  =       torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0)			,		size=target_sizes[idx]			,		mode="""bilinear"""			,		align_corners=UpperCamelCase__)
																									snake_case__	  =       resized_logits[0].argmax(dim=0)
																									semantic_segmentation.append(UpperCamelCase__)
											else:
																		snake_case__	  =       logits.argmax(dim=1)
																		snake_case__	  =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

											return semantic_segmentation








def _UpperCAmelCase   (	a						:	int  ,					a						:	Optional[Any] ):
							# "extended trapezoidal rule"
							# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
							snake_case__	  =       (boundary[1] - boundary[0]) / steps
							snake_case__	  =       boundary[0]
							snake_case__	  =       boundary[1]
							snake_case__	  =       make_points(a  ,					a  ,					a )
							snake_case__	  =       0.0
							y += (h / 2.0) * f(a )
							for i in x_i:
														# print(i)
														y += h * f(a )
							y += (h / 2.0) * f(a )
							return y

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       a + h
							while x < (b - h):
														yield x
														snake_case__	  =       x + h

def _UpperCAmelCase   (	a						:	Any ):  # enter your function here
							snake_case__	  =       (x - 0) * (x - 0)
							return y

def _UpperCAmelCase   (	):
							snake_case__	  =       0.0  # Lower bound of integration
							snake_case__	  =       1.0  # Upper bound of integration
							snake_case__	  =       10.0  # define number of steps or resolution
							snake_case__	  =       [a, b]  # define boundary of integration
							snake_case__	  =       method_a(a  ,					a )
							print(F'''y = {y}''' )


if __name__ == "__main__":
	main()
