






"""simple docstring"""
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


lowerCAmelCase__    						=			logging.get_logger(__name__)





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				def constraint_to_multiple_of(lowerCamelCase,						lowerCamelCase,						lowerCamelCase=0,						lowerCamelCase=None       ):
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    round(val / multiple       ) * multiple

								if max_val is not None and x > max_val:
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    math.floor(val / multiple       ) * multiple

								if x < min_val:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    math.ceil(val / multiple       ) * multiple

								return x

				_SCREAMING_SNAKE_CASE   :		Dict       		=    (output_size, output_size) if isinstance(lowerCamelCase,						lowerCamelCase       ) else output_size

				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    get_image_size(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    output_size

				# determine new height and width
				_SCREAMING_SNAKE_CASE   :		Any       		=    output_height / input_height
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    output_width / input_width

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

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    constraint_to_multiple_of(scale_height * input_height,						multiple=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    constraint_to_multiple_of(scale_width * input_width,						multiple=lowerCamelCase       )

				return (new_height, new_width)






class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					int 					=				['pixel_values']







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







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = PILImageResampling.BICUBIC   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							np.ndarray:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_size_dict(lowerCAmelCase_							)
										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()}"""							)
										_SCREAMING_SNAKE_CASE   :		str       		=    get_resize_output_image_size(
										    lowerCAmelCase_   ,				output_size=(size['height'], size['width'])   ,				keep_aspect_ratio=lowerCAmelCase_   ,				multiple=lowerCAmelCase_   ,				)
										return resize(lowerCAmelCase_   ,				size=lowerCAmelCase_   ,				resample=lowerCAmelCase_   ,				data_format=lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							int:
										return rescale(lowerCAmelCase_   ,				scale=lowerCAmelCase_   ,				data_format=lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							np.ndarray:
										return normalize(lowerCAmelCase_   ,				mean=lowerCAmelCase_   ,				std=lowerCAmelCase_   ,				data_format=lowerCAmelCase_   ,				**lowerCAmelCase_							)







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

										_SCREAMING_SNAKE_CASE   :		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 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.
										_SCREAMING_SNAKE_CASE   :		Dict       		=    [to_numpy_array(lowerCAmelCase_							) for image in images]

										if do_resize:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [self.resize(image=lowerCAmelCase_   ,				size=lowerCAmelCase_   ,				resample=lowerCAmelCase_							) for image in images]

										if do_rescale:
														_SCREAMING_SNAKE_CASE   :		str       		=    [self.rescale(image=lowerCAmelCase_   ,				scale=lowerCAmelCase_							) for image in images]

										if do_normalize:
														_SCREAMING_SNAKE_CASE   :		Any       		=    [self.normalize(image=lowerCAmelCase_   ,				mean=lowerCAmelCase_   ,				std=lowerCAmelCase_							) for image in images]

										_SCREAMING_SNAKE_CASE   :		Dict       		=    [to_channel_dimension_format(lowerCAmelCase_   ,				lowerCAmelCase_							) for image in images]

										_SCREAMING_SNAKE_CASE   :		int       		=    {'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase_   ,				tensor_type=lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    outputs.logits

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

														if is_torch_tensor(lowerCAmelCase_							):
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    target_sizes.numpy()

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    []

														for idx in range(len(lowerCAmelCase_							)							):
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.nn.functional.interpolate(
																		    logits[idx].unsqueeze(dim=0							)   ,				size=target_sizes[idx]   ,				mode='bilinear'   ,				align_corners=lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    resized_logits[0].argmax(dim=0							)
																		semantic_segmentation.append(lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    logits.argmax(dim=1							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

										return semantic_segmentation









"""simple docstring"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

