

"""simple docstring"""



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


SCREAMING_SNAKE_CASE_					:	str							  =						logging.get_logger(__name__)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					["pixel_values"]

    def __init__(     self: str    ,						UpperCamelCase: bool = True    ,						UpperCamelCase: int = 32    ,						UpperCamelCase: Dict=PILImageResampling.BILINEAR    ,						UpperCamelCase: bool = True    ,						**UpperCamelCase: Any    ,						):






         """simple docstring"""


         A__										=  do_resize
         A__										=  do_rescale
         A__										=  size_divisor
         A__										=  resample
         super().__init__(**UpperCamelCase       )

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: np.ndarray    ,						UpperCamelCase: int    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Optional[ChannelDimension] = None    ,						**UpperCamelCase: Dict       ):






         """simple docstring"""


         A__			,				A__										=  get_image_size(UpperCamelCase       )
         # Rounds the height and width down to the closest multiple of size_divisor
         A__										=  height // size_divisor * size_divisor
         A__										=  width // size_divisor * size_divisor
         A__										=  resize(UpperCamelCase    ,						(new_h, new_w)    ,						resample=UpperCamelCase    ,						data_format=UpperCamelCase    ,						**UpperCamelCase       )
         return image

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: np.ndarray    ,						UpperCamelCase: float    ,						UpperCamelCase: Optional[ChannelDimension] = None    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         return rescale(image=UpperCamelCase    ,						scale=UpperCamelCase    ,						data_format=UpperCamelCase    ,						**UpperCamelCase       )





    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,						UpperCamelCase: Optional[bool] = None    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: Tuple=None    ,						UpperCamelCase: Optional[bool] = None    ,						UpperCamelCase: Optional[Union[TensorType, str]] = None    ,						UpperCamelCase: ChannelDimension = ChannelDimension.FIRST    ,						**UpperCamelCase: Union[str, Any]    ,						):






         """simple docstring"""


         A__										=  do_resize if do_resize is not None else self.do_resize
         A__										=  do_rescale if do_rescale is not None else self.do_rescale
         A__										=  size_divisor if size_divisor is not None else self.size_divisor
         A__										=  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"""       )

         A__										=  make_list_of_images(UpperCamelCase       )

         if not valid_images(UpperCamelCase       ):
              raise ValueError("""Invalid image(s)"""       )

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

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

         if do_rescale:
              A__										=  [self.rescale(UpperCamelCase    ,						scale=1 / 2_55       ) 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       )









"""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


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    '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						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


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

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






