






"""simple docstring"""






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,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE    :    Any        =							logging.get_logger(__name__)





class    snake_case_							(     __SCREAMING_SNAKE_CASE							):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:				int       =       ["""pixel_values"""]


	def __init__(   self	,       __a = True	,       __a = None	,       __a = PILImageResampling.BILINEAR	,       __a = True	,       __a = 1 / 255	,       __a = True	,       __a = None	,       __a = True	,       **__a	,       ):


					"""simple docstring"""

					super().__init__(**UpperCamelCase__    )
					A__							=							size if size is not None else {'''shortest_edge''': 224}
					A__							=							get_size_dict(UpperCamelCase__	,       default_to_square=UpperCamelCase__    )
					A__							=							crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
					A__							=							get_size_dict(UpperCamelCase__	,       param_name='crop_size'    )

					A__							=							do_resize
					A__							=							size
					A__							=							resample
					A__							=							do_rescale
					A__							=							rescale_factor
					A__							=							do_center_crop
					A__							=							crop_size
					A__							=							do_flip_channel_order


	def    _UpperCAmelCase    (   self	,       __a	,       __a	,       __a = PIL.Image.BILINEAR	,       __a = None	,       **__a	,       ):


					"""simple docstring"""

					A__							=							get_size_dict(UpperCamelCase__	,       default_to_square=UpperCamelCase__    )
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''    )
					A__							=							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    _UpperCAmelCase    (   self	,       __a	,       __a	,       __a = None	,       **__a	,       ):


					"""simple docstring"""

					A__							=							get_size_dict(UpperCamelCase__    )
					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()}'''    )
					return center_crop(UpperCamelCase__	,       size=(size['height'], size['width'])	,       data_format=UpperCamelCase__	,       **UpperCamelCase__    )


	def    _UpperCAmelCase    (   self	,       __a	,       __a	,       __a = None	,       **__a	,       ):


					"""simple docstring"""

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


	def    _UpperCAmelCase    (   self	,       __a	,       __a = None    ):


					"""simple docstring"""

					return flip_channel_order(UpperCamelCase__	,       data_format=UpperCamelCase__    )


	def    _UpperCAmelCase    (   self	,       __a	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = None	,       __a = ChannelDimension.FIRST	,       **__a	,       ):


					"""simple docstring"""

					A__							=							do_resize if do_resize is not None else self.do_resize
					A__							=							resample if resample is not None else self.resample
					A__							=							do_rescale if do_rescale is not None else self.do_rescale
					A__							=							rescale_factor if rescale_factor is not None else self.rescale_factor
					A__							=							do_center_crop if do_center_crop is not None else self.do_center_crop
					A__							=							(
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

					A__							=							size if size is not None else self.size
					A__							=							get_size_dict(UpperCamelCase__	,       default_to_square=UpperCamelCase__    )
					A__							=							crop_size if crop_size is not None else self.crop_size
					A__							=							get_size_dict(UpperCamelCase__	,       param_name='crop_size'    )

					A__							=							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_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

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

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

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

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

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

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									A__							=							[self.flip_channel_order(image=UpperCamelCase__    ) for image in images]

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

					A__							=							{'''pixel_values''': images}
					return BatchFeature(data=UpperCamelCase__	,       tensor_type=UpperCamelCase__    )






	def    _UpperCAmelCase    (   self	,       __a	,       __a = None    ):


					"""simple docstring"""

					A__							=							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__    ):
													A__							=							target_sizes.numpy()

									A__							=							[]

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

					return semantic_segmentation




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


