






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase											=      logging.get_logger(__name__)


if is_vision_available():
       import PIL




class 	lowerCamelCase__				(							__UpperCAmelCase ):
      lowerCamelCase_							:   int											=	['pixel_values']






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


       """simple docstring"""






       super().__init__(**_snake_case		)
       lowerCamelCase_							:    str							=     size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_							:    str							=     get_size_dict(_snake_case		,		default_to_square=_snake_case		)
       lowerCamelCase_							:    List[Any]							=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_							:    List[str]							=     get_size_dict(_snake_case		)

       lowerCamelCase_							:    List[Any]							=     do_resize
       lowerCamelCase_							:    int							=     size
       lowerCamelCase_							:    int							=     resample
       lowerCamelCase_							:    int							=     do_center_crop
       lowerCamelCase_							:    Optional[int]							=     crop_size
       lowerCamelCase_							:    int							=     do_rescale
       lowerCamelCase_							:    str							=     rescale_factor
       lowerCamelCase_							:    Union[str, Any]							=     do_normalize
       lowerCamelCase_							:    List[str]							=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_							:    Any							=     image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_							:    str							=     do_convert_rgb






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[str]		,		_snake_case					:    Union[str, Any]		,		_snake_case					:    Optional[Any] = PILImageResampling.BICUBIC		,		_snake_case					:    Union[str, Any] = None		,		**_snake_case					:    str		,		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     get_size_dict(_snake_case		,		default_to_square=_snake_case		)
       lowerCamelCase_							:    List[str]							=     get_resize_output_image_size(
           _snake_case		,		size=(size['height'], size['width'])		,		default_to_square=_snake_case		)
       return resize(_snake_case		,		size=_snake_case		,		resample=_snake_case		,		data_format=_snake_case		,		**_snake_case		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    int		,		_snake_case					:    Any		,		_snake_case					:    Optional[int] = None		,		**_snake_case					:    Tuple		,		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    str							=     get_size_dict(_snake_case		)
       return center_crop(_snake_case		,		size=(size['height'], size['width'])		,		data_format=_snake_case		,		**_snake_case		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		,		_snake_case					:    Optional[int]		,		_snake_case					:    Tuple		,		_snake_case					:    int = None		,		**_snake_case					:    Optional[int]		,		)       ->   str:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    int		,		_snake_case					:    Union[str, Any]		,		_snake_case					:    Optional[int]		,		_snake_case					:    Any		,		_snake_case					:    Union[str, Any] = None		,		**_snake_case					:    Any		,		)       ->   Tuple:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    List[Any]		,		_snake_case					:    Any = None		,		_snake_case					:    List[Any] = None		,		_snake_case					:    Optional[Any] = None		,		_snake_case					:    List[Any] = None		,		_snake_case					:    Union[str, Any] = None		,		_snake_case					:    List[Any] = None		,		_snake_case					:    int = None		,		_snake_case					:    Tuple = None		,		_snake_case					:    str = None		,		_snake_case					:    Any = None		,		_snake_case					:    Dict = None		,		_snake_case					:    int = None		,		_snake_case					:    List[str] = ChannelDimension.FIRST		,		**_snake_case					:    Any		,		)       ->   List[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_							:    Dict							=     size if size is not None else self.size
       lowerCamelCase_							:    List[Any]							=     get_size_dict(_snake_case		,		default_to_square=_snake_case		)
       lowerCamelCase_							:    int							=     resample if resample is not None else self.resample
       lowerCamelCase_							:    List[str]							=     do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_							:    str							=     crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_							:    int							=     get_size_dict(_snake_case		)
       lowerCamelCase_							:    Dict							=     do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_							:    Union[str, Any]							=     rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_							:    int							=     do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_							:    int							=     image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_							:    List[Any]							=     image_std if image_std is not None else self.image_std
       lowerCamelCase_							:    List[Any]							=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_							:    int							=     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_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.'		)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
        lowerCamelCase_							:    int							=     [convert_to_rgb(_snake_case		) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_							:    str							=     [to_numpy_array(_snake_case		) for image in images]

       if do_resize:
        lowerCamelCase_							:    Optional[Any]							=     [self.resize(image=_snake_case		,		size=_snake_case		,		resample=_snake_case		) for image in images]

       if do_center_crop:
        lowerCamelCase_							:    Dict							=     [self.center_crop(image=_snake_case		,		size=_snake_case		) for image in images]

       if do_rescale:
        lowerCamelCase_							:    Any							=     [self.rescale(image=_snake_case		,		scale=_snake_case		) for image in images]

       if do_normalize:
        lowerCamelCase_							:    Optional[Any]							=     [self.normalize(image=_snake_case		,		mean=_snake_case		,		std=_snake_case		) for image in images]

       lowerCamelCase_							:    int							=     [to_channel_dimension_format(_snake_case		,		_snake_case		) for image in images]

       lowerCamelCase_							:    Any							=     {'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


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            warnings.warn(
                '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MobileNetV2ImageProcessor instead.''',							A,							)
            super().__init__(*A,							**A   )






