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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[Any]						=      """segformer"""







    def __init__(	self , __lowercase=3 , __lowercase=4 , __lowercase=[2, 2, 2, 2] , __lowercase=[8, 4, 2, 1] , __lowercase=[32, 64, 160, 256] , __lowercase=[7, 3, 3, 3] , __lowercase=[4, 2, 2, 2] , __lowercase=[1, 2, 5, 8] , __lowercase=[4, 4, 4, 4] , __lowercase="gelu" , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=256 , __lowercase=255 , **__lowercase , )       ->	List[Any]:
        super().__init__(**__lowercase)

        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.''' , __lowercase , )

        __UpperCamelCase     :Tuple			  =		num_channels
        __UpperCamelCase     :int			  =		num_encoder_blocks
        __UpperCamelCase     :Tuple			  =		depths
        __UpperCamelCase     :Dict			  =		sr_ratios
        __UpperCamelCase     :List[Any]			  =		hidden_sizes
        __UpperCamelCase     :str			  =		patch_sizes
        __UpperCamelCase     :Tuple			  =		strides
        __UpperCamelCase     :str			  =		mlp_ratios
        __UpperCamelCase     :str			  =		num_attention_heads
        __UpperCamelCase     :Optional[Any]			  =		hidden_act
        __UpperCamelCase     :Optional[Any]			  =		hidden_dropout_prob
        __UpperCamelCase     :str			  =		attention_probs_dropout_prob
        __UpperCamelCase     :List[Any]			  =		classifier_dropout_prob
        __UpperCamelCase     :Optional[int]			  =		initializer_range
        __UpperCamelCase     :Dict			  =		drop_path_rate
        __UpperCamelCase     :List[Any]			  =		layer_norm_eps
        __UpperCamelCase     :Optional[int]			  =		decoder_hidden_size
        __UpperCamelCase     :Union[str, Any]			  =		kwargs.get('''reshape_last_stage''' , __lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		semantic_loss_ignore_index


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Any						=      version.parse("""1.11""" )







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







    @property
    def 			UpperCamelCase__   (	self)       ->	float:
        return 1E-4







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






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






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







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





