"""simple docstring"""




import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  str  							=						'segformer'





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=[2, 2, 2, 2]						,__UpperCAmelCase=[8, 4, 2, 1]						,__UpperCAmelCase=[32, 64, 1_60, 2_56]						,__UpperCAmelCase=[7, 3, 3, 3]						,__UpperCAmelCase=[4, 2, 2, 2]						,__UpperCAmelCase=[1, 2, 5, 8]						,__UpperCAmelCase=[4, 4, 4, 4]						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=0.1						,__UpperCAmelCase=1e-6						,__UpperCAmelCase=2_56						,__UpperCAmelCase=2_55						,**__UpperCAmelCase						,)    -> Any:
													super().__init__(**__UpperCAmelCase   )

													if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
																				warnings.warn(
																				    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
																				    ' removed, as the behaviour will default to that of reshape_last_stage = True.'						,__UpperCAmelCase						,)

													A__		   =							num_channels
													A__		   =							num_encoder_blocks
													A__		   =							depths
													A__		   =							sr_ratios
													A__		   =							hidden_sizes
													A__		   =							patch_sizes
													A__		   =							strides
													A__		   =							mlp_ratios
													A__		   =							num_attention_heads
													A__		   =							hidden_act
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							classifier_dropout_prob
													A__		   =							initializer_range
													A__		   =							drop_path_rate
													A__		   =							layer_norm_eps
													A__		   =							decoder_hidden_size
													A__		   =							kwargs.get('reshape_last_stage'						,__UpperCAmelCase   )
													A__		   =							semantic_loss_ignore_index






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  str  							=						version.parse('1.11'       )





						@property
						def  snake_case__		(				self   )    -> Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
													    ]   )





						@property
						def  snake_case__		(				self   )    -> float:
													return 1e-4





						@property
						def  snake_case__		(				self   )    -> int:
													return 12






"""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


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													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(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													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 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.
													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_normalize:
																				A__		   =							[self.normalize(image=__UpperCAmelCase   ) for image in images]

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

																				# flatten to (batch_size, height*width)
																				A__		   =							images.shape[0]
																				A__		   =							images.reshape(__UpperCAmelCase						,-1   )

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

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





