
from typing import Dict, Iterable, List, Optional, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   __UpperCAmelCase					):
       lowercase_		:						List[str]   							=  ['pixel_values']







       def __init__(						self  :  str      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,				**snake_case__  :  Optional[Any]      ,				):
        """simple docstring"""







        super().__init__(**snake_case__ )
        __lowerCAmelCase         =							size if size is not None else {"shortest_edge": 224}
        __lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
        __lowerCAmelCase         =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        __lowerCAmelCase         =							get_size_dict(snake_case__      ,				param_name="crop_size" )

        __lowerCAmelCase         =							do_resize
        __lowerCAmelCase         =							size
        __lowerCAmelCase         =							resample
        __lowerCAmelCase         =							do_center_crop
        __lowerCAmelCase         =							crop_size
        __lowerCAmelCase         =							do_rescale
        __lowerCAmelCase         =							rescale_factor
        __lowerCAmelCase         =							do_normalize
        __lowerCAmelCase         =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __lowerCAmelCase         =							image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Union[str, Any]      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
         __lowerCAmelCase         =							int((256 / 224) * size["shortest_edge"] )
         __lowerCAmelCase         =							get_resize_output_image_size(snake_case__      ,				size=snake_case__      ,				default_to_square=snake_case__ )
         __lowerCAmelCase         =							{"height": output_size[0], "width": output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}" )
        return resize(
            snake_case__      ,				size=(size_dict["height"], size_dict["width"])      ,				resample=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







       def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[Any]      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							get_size_dict(snake_case__ )
        if "height" not in size or "width" not in size:
         raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}" )
        return center_crop(snake_case__      ,				size=(size["height"], size["width"])      ,				data_format=snake_case__      ,				**snake_case__ )







       def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Tuple      ,				):
        """simple docstring"""







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







       def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  List[Any]      ,				):
        """simple docstring"""







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







       def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  ImageInput      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  PILImageResampling = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[float] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = None      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = None      ,				snake_case__  :  Optional[TensorType] = None      ,				snake_case__  :  ChannelDimension = ChannelDimension.FIRST      ,				**snake_case__  :  Optional[Any]      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							do_resize if do_resize is not None else self.do_resize
        __lowerCAmelCase         =							resample if resample is not None else self.resample
        __lowerCAmelCase         =							do_center_crop if do_center_crop is not None else self.do_center_crop
        __lowerCAmelCase         =							do_rescale if do_rescale is not None else self.do_rescale
        __lowerCAmelCase         =							rescale_factor if rescale_factor is not None else self.rescale_factor
        __lowerCAmelCase         =							do_normalize if do_normalize is not None else self.do_normalize
        __lowerCAmelCase         =							image_mean if image_mean is not None else self.image_mean
        __lowerCAmelCase         =							image_std if image_std is not None else self.image_std

        __lowerCAmelCase         =							size if size is not None else self.size
        __lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
        __lowerCAmelCase         =							crop_size if crop_size is not None else self.crop_size
        __lowerCAmelCase         =							get_size_dict(snake_case__      ,				param_name="crop_size" )

        __lowerCAmelCase         =							make_list_of_images(snake_case__ )

        if not valid_images(snake_case__ ):
         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.
        __lowerCAmelCase         =							[to_numpy_array(snake_case__ ) for image in images]

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

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

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

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

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

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








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "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       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								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."      ,				snake_case__      ,				)

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

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






