
'''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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_			:			Dict			   =     logging.get_logger(__name__)







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[Any]							      =      ["""pixel_values"""]



     def __init__(self						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = PILImageResampling.BILINEAR						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = 1 / 2_5_5						,	lowerCAmelCase__ = True						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          super().__init__(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Any         =    size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
          _UpperCAmelCase					:  Optional[int]         =    get_size_dict(lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    do_resize
          _UpperCAmelCase					:  int         =    do_rescale
          _UpperCAmelCase					:  int         =    do_normalize
          _UpperCAmelCase					:  Optional[int]         =    size
          _UpperCAmelCase					:  Dict         =    resample
          _UpperCAmelCase					:  List[Any]         =    rescale_factor
          _UpperCAmelCase					:  List[str]         =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _UpperCAmelCase					:  int         =    image_std if image_std is not None else IMAGENET_STANDARD_STD



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = PILImageResampling.BILINEAR						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    get_size_dict(lowerCAmelCase__	)
          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 resize(
              lowerCAmelCase__						,	size=(size["""height"""], size["""width"""])						,	resample=lowerCAmelCase__						,	data_format=lowerCAmelCase__						,	**lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__	):
          return rescale(lowerCAmelCase__						,	scale=lowerCAmelCase__						,	data_format=lowerCAmelCase__						,	**lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          return normalize(lowerCAmelCase__						,	mean=lowerCAmelCase__						,	std=lowerCAmelCase__						,	data_format=lowerCAmelCase__						,	**lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = ChannelDimension.FIRST						,	**lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase					:  int         =    do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase					:  List[Any]         =    do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase					:  Optional[int]         =    resample if resample is not None else self.resample
          _UpperCAmelCase					:  Union[str, Any]         =    rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase					:  Dict         =    image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase					:  Any         =    image_std if image_std is not None else self.image_std

          _UpperCAmelCase					:  Any         =    size if size is not None else self.size
          _UpperCAmelCase					:  int         =    get_size_dict(lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    make_list_of_images(lowerCAmelCase__	)

          if not valid_images(lowerCAmelCase__	):
               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."""	)

          # All transformations expect numpy arrays.
          _UpperCAmelCase					:  List[str]         =    [to_numpy_array(lowerCAmelCase__	) for image in images]

          if do_resize:
               _UpperCAmelCase					:  Tuple         =    [self.resize(image=lowerCAmelCase__						,	size=lowerCAmelCase__						,	resample=lowerCAmelCase__	) for image in images]

          if do_rescale:
               _UpperCAmelCase					:  Optional[int]         =    [self.rescale(image=lowerCAmelCase__						,	scale=lowerCAmelCase__	) for image in images]

          if do_normalize:
               _UpperCAmelCase					:  List[Any]         =    [self.normalize(image=lowerCAmelCase__						,	mean=lowerCAmelCase__						,	std=lowerCAmelCase__	) for image in images]

          _UpperCAmelCase					:  Union[str, Any]         =    [to_channel_dimension_format(lowerCAmelCase__						,	lowerCAmelCase__	) for image in images]

          _UpperCAmelCase					:  Tuple         =    {"""pixel_values""": images}
          return BatchFeature(data=lowerCAmelCase__						,	tensor_type=lowerCAmelCase__	)





'''simple docstring'''






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


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

