


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


A :	int												=     logging.get_logger(__name__)



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






					A__    =				['''pixel_values''']

					def __init__(self					:		Tuple   ,      _UpperCAmelCase					:		Optional[int] = True   ,      _UpperCAmelCase					:		List[str] = None   ,      _UpperCAmelCase					:		int = PILImageResampling.BILINEAR   ,      _UpperCAmelCase					:		Optional[int] = True   ,      _UpperCAmelCase					:		Dict = 1 / 255   ,      _UpperCAmelCase					:		Optional[Any] = True   ,      _UpperCAmelCase					:		Dict = None   ,      _UpperCAmelCase					:		Tuple = None   ,      **_UpperCAmelCase					:		Optional[int]   ,      )		->     None:




							"""simple docstring"""


							super().__init__(**snake_case_	)
							lowercase__      =	size if size is not None else {'''height''': 224, '''width''': 224}
							lowercase__      =	get_size_dict(snake_case_	)
							lowercase__      =	do_resize
							lowercase__      =	do_rescale
							lowercase__      =	do_normalize
							lowercase__      =	size
							lowercase__      =	resample
							lowercase__      =	rescale_factor
							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

					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		Tuple   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		List[Any] = PILImageResampling.BILINEAR   ,      _UpperCAmelCase					:		int = None   ,      **_UpperCAmelCase					:		Any   ,      )		->     np.ndarray:




							"""simple docstring"""


							lowercase__      =	get_size_dict(snake_case_	)
							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(
							    snake_case_   ,      size=(size["""height"""], size["""width"""])   ,      resample=snake_case_   ,      data_format=snake_case_   ,      **snake_case_	)

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




							"""simple docstring"""


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

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




							"""simple docstring"""


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


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




							"""simple docstring"""


							lowercase__      =	do_resize if do_resize is not None else self.do_resize
							lowercase__      =	do_rescale if do_rescale is not None else self.do_rescale
							lowercase__      =	do_normalize if do_normalize is not None else self.do_normalize
							lowercase__      =	resample if resample is not None else self.resample
							lowercase__      =	rescale_factor if rescale_factor is not None else self.rescale_factor
							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__      =	size if size is not None else self.size
							lowercase__      =	get_size_dict(snake_case_	)

							lowercase__      =	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_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

							# All transformations expect numpy arrays.
							lowercase__      =	[to_numpy_array(snake_case_	) for image in images]

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

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

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

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

							lowercase__      =	{'''pixel_values''': images}
							return BatchFeature(data=snake_case_   ,      tensor_type=snake_case_	)





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	)

