
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


snake_case				:	Optional[int]							   =						logging.get_logger(__name__)





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					['pixel_values']







			def __init__(       self		,     _a = True		,     _a = None		,     _a = PILImageResampling.BILINEAR		,     _a = True		,     _a = 1 / 255		,     _a = True		,     _a = None		,     _a = None		,     **_a		,     ):
				super().__init__(**_a	)
				__magic_name__	:	str			     =  size if size is not None else {"height": 224, "width": 224}
				__magic_name__	:	List[str]			     =  get_size_dict(_a	)
				__magic_name__	:	List[str]			     =  do_resize
				__magic_name__	:	int			     =  do_rescale
				__magic_name__	:	Optional[Any]			     =  do_normalize
				__magic_name__	:	Union[str, Any]			     =  size
				__magic_name__	:	List[str]			     =  resample
				__magic_name__	:	List[Any]			     =  rescale_factor
				__magic_name__	:	int			     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__magic_name__	:	Optional[Any]			     =  image_std if image_std is not None else IMAGENET_STANDARD_STD







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a = PILImageResampling.BILINEAR		,     _a = None		,     **_a		,     ):
				__magic_name__	:	Any			     =  get_size_dict(_a	)
				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(
				    _a		,     size=(size["height"], size["width"])		,     resample=_a		,     data_format=_a		,     **_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a = None		,     **_a	):
				return rescale(_a		,     scale=_a		,     data_format=_a		,     **_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a = None		,     **_a		,     ):
				return normalize(_a		,     mean=_a		,     std=_a		,     data_format=_a		,     **_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = ChannelDimension.FIRST		,     **_a		,     ):
				__magic_name__	:	Tuple			     =  do_resize if do_resize is not None else self.do_resize
				__magic_name__	:	Union[str, Any]			     =  do_rescale if do_rescale is not None else self.do_rescale
				__magic_name__	:	Tuple			     =  do_normalize if do_normalize is not None else self.do_normalize
				__magic_name__	:	Any			     =  resample if resample is not None else self.resample
				__magic_name__	:	str			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
				__magic_name__	:	Optional[int]			     =  image_mean if image_mean is not None else self.image_mean
				__magic_name__	:	List[Any]			     =  image_std if image_std is not None else self.image_std

				__magic_name__	:	Optional[int]			     =  size if size is not None else self.size
				__magic_name__	:	List[str]			     =  get_size_dict(_a	)

				__magic_name__	:	str			     =  make_list_of_images(_a	)

				if not valid_images(_a	):
					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.
				__magic_name__	:	Dict			     =  [to_numpy_array(_a	) for image in images]

				if do_resize:
					__magic_name__	:	Optional[int]			     =  [self.resize(image=_a		,     size=_a		,     resample=_a	) for image in images]

				if do_rescale:
					__magic_name__	:	int			     =  [self.rescale(image=_a		,     scale=_a	) for image in images]

				if do_normalize:
					__magic_name__	:	Optional[int]			     =  [self.normalize(image=_a		,     mean=_a		,     std=_a	) for image in images]

				__magic_name__	:	Tuple			     =  [to_channel_dimension_format(_a		,     _a	) for image in images]

				__magic_name__	:	Tuple			     =  {"pixel_values": images}
				return BatchFeature(data=_a		,     tensor_type=_a	)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case				:	Optional[int]							   =						{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case				:	Optional[int]							   =						[
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
