


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 center_crop, 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, is_vision_available, logging


if is_vision_available():
	import PIL


A :	List[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				['''pixel_values''']

					def __init__(self					:		Tuple   ,      _UpperCAmelCase					:		bool = True   ,      _UpperCAmelCase					:		Dict[str, int] = None   ,      _UpperCAmelCase					:		PILImageResampling = PIL.Image.NEAREST   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		Dict[str, int] = None   ,      _UpperCAmelCase					:		Union[int, float] = 1 / 255   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		bool = True   ,      _UpperCAmelCase					:		bool = True   ,      _UpperCAmelCase					:		Optional[Union[float, List[float]]] = None   ,      _UpperCAmelCase					:		Optional[Union[float, List[float]]] = None   ,      _UpperCAmelCase					:		bool = True   ,      **_UpperCAmelCase					:		Any   ,      )		->     None:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	size if size is not None else {"""height""": 346, """width""": 346}
							lowercase__      =	get_size_dict(_UpperCAmelCase	)
							lowercase__      =	crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
							lowercase__      =	get_size_dict(_UpperCAmelCase   ,      param_name="""crop_size"""	)

							lowercase__      =	do_resize
							lowercase__      =	size
							lowercase__      =	resample
							lowercase__      =	do_center_crop
							lowercase__      =	crop_size
							lowercase__      =	do_rescale
							lowercase__      =	rescale_factor
							lowercase__      =	rescale_offset
							lowercase__      =	do_normalize
							lowercase__      =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase__      =	image_std if image_std is not None else IMAGENET_STANDARD_STD
							lowercase__      =	include_top

					def 						lowerCamelCase__  (self					:		Optional[int]   ,      _UpperCAmelCase					:		np.ndarray   ,      _UpperCAmelCase					:		Dict[str, int]   ,      _UpperCAmelCase					:		PILImageResampling = PIL.Image.NEAREST   ,      _UpperCAmelCase					:		Optional[Union[str, ChannelDimension]] = None   ,      **_UpperCAmelCase					:		Tuple   ,      )		->     np.ndarray:




							"""simple docstring"""


							lowercase__      =	get_size_dict(_UpperCAmelCase	)
							if "height" not in size or "width" not in size:
									raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
							return resize(
							    _UpperCAmelCase   ,      size=(size["""height"""], size["""width"""])   ,      resample=_UpperCAmelCase   ,      data_format=_UpperCAmelCase   ,      **_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		np.ndarray   ,      _UpperCAmelCase					:		Dict[str, int]   ,      _UpperCAmelCase					:		Optional[Union[str, ChannelDimension]] = None   ,      **_UpperCAmelCase					:		Union[str, Any]   ,      )		->     np.ndarray:




							"""simple docstring"""


							lowercase__      =	get_size_dict(_UpperCAmelCase	)
							if "height" not in size or "width" not in size:
									raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
							return center_crop(_UpperCAmelCase   ,      size=(size["""height"""], size["""width"""])   ,      data_format=_UpperCAmelCase   ,      **_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		np.ndarray   ,      _UpperCAmelCase					:		Union[int, float]   ,      _UpperCAmelCase					:		bool = True   ,      _UpperCAmelCase					:		Optional[Union[str, ChannelDimension]] = None   ,      **_UpperCAmelCase					:		str   ,      )		->     Any:




							"""simple docstring"""


							if offset:
									lowercase__      =	(image - 127.5) * scale
									if data_format is not None:
											lowercase__      =	to_channel_dimension_format(_UpperCAmelCase   ,      _UpperCAmelCase	)
									lowercase__      =	rescaled_image.astype(np.floataa	)
							else:
									lowercase__      =	rescale(_UpperCAmelCase   ,      scale=_UpperCAmelCase   ,      data_format=_UpperCAmelCase   ,      **_UpperCAmelCase	)
							return rescaled_image

					def 						lowerCamelCase__  (self					:		Optional[int]   ,      _UpperCAmelCase					:		np.ndarray   ,      _UpperCAmelCase					:		Union[float, List[float]]   ,      _UpperCAmelCase					:		Union[float, List[float]]   ,      _UpperCAmelCase					:		Optional[Union[str, ChannelDimension]] = None   ,      **_UpperCAmelCase					:		str   ,      )		->     np.ndarray:




							"""simple docstring"""


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


					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		ImageInput   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		Dict[str, int] = None   ,      _UpperCAmelCase					:		Tuple=None   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		Dict[str, int] = None   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		float = None   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		Optional[Union[float, List[float]]] = None   ,      _UpperCAmelCase					:		Optional[Union[float, List[float]]] = None   ,      _UpperCAmelCase					:		bool = None   ,      _UpperCAmelCase					:		Optional[Union[str, TensorType]] = None   ,      _UpperCAmelCase					:		ChannelDimension = ChannelDimension.FIRST   ,      **_UpperCAmelCase					:		Any   ,      )		->     PIL.Image.Image:




							"""simple docstring"""


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

							lowercase__      =	size if size is not None else self.size
							lowercase__      =	get_size_dict(_UpperCAmelCase	)
							lowercase__      =	crop_size if crop_size is not None else self.crop_size
							lowercase__      =	get_size_dict(_UpperCAmelCase   ,      param_name="""crop_size"""	)

							lowercase__      =	make_list_of_images(_UpperCAmelCase	)

							if not valid_images(_UpperCAmelCase	):
									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 or resample is None:
									raise ValueError("""Size and resample 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.
							lowercase__      =	[to_numpy_array(_UpperCAmelCase	) for image in images]

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

							if do_center_crop:
									lowercase__      =	[self.center_crop(image=_UpperCAmelCase   ,      size=_UpperCAmelCase	) for image in images]

							if do_rescale:
									lowercase__      =	[self.rescale(image=_UpperCAmelCase   ,      scale=_UpperCAmelCase   ,      offset=_UpperCAmelCase	) for image in images]

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

							if include_top:
									lowercase__      =	[self.normalize(image=_UpperCAmelCase   ,      mean=[0, 0, 0]   ,      std=_UpperCAmelCase	) for image in images]

							lowercase__      =	[to_channel_dimension_format(_UpperCAmelCase   ,      _UpperCAmelCase	) for image in images]

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





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A :	Optional[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      *_UpperCAmelCase					:		Union[str, Any]   ,      **_UpperCAmelCase					:		Any	)		->     None:




							"""simple docstring"""


							warnings.warn(
							    """The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV1ImageProcessor instead."""   ,      _UpperCAmelCase   ,      )
							super().__init__(*_UpperCAmelCase   ,      **_UpperCAmelCase	)

