



"""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 (
    center_crop,
    get_resize_output_image_size,
    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       	=						logging.get_logger(__name__)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''pixel_values''']



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


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase   )
             UpperCAmelCase_      			=							size if size is not None else {"shortest_edge": 256}
             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase		,    default_to_square=_UpperCAmelCase   )
             UpperCAmelCase_      			=							crop_size if crop_size is not None else {"height": 224, "width": 224}
             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase   )
             UpperCAmelCase_      			=							do_resize
             UpperCAmelCase_      			=							size
             UpperCAmelCase_      			=							resample
             UpperCAmelCase_      			=							do_center_crop
             UpperCAmelCase_      			=							crop_size
             UpperCAmelCase_      			=							do_rescale
             UpperCAmelCase_      			=							rescale_factor
             UpperCAmelCase_      			=							do_normalize
             UpperCAmelCase_      			=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_      			=							image_std if image_std is not None else IMAGENET_STANDARD_STD



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


             '''simple docstring'''




             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase		,    default_to_square=_UpperCAmelCase   )
             if "shortest_edge" not in size:
                   raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
             UpperCAmelCase_      			=							get_resize_output_image_size(_UpperCAmelCase		,    size=size["shortest_edge"]		,    default_to_square=_UpperCAmelCase   )
             return resize(_UpperCAmelCase		,    size=_UpperCAmelCase		,    resample=_UpperCAmelCase		,    data_format=_UpperCAmelCase		,    **_UpperCAmelCase   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase   )
             return center_crop(_UpperCAmelCase		,    size=(size["height"], size["width"])		,    data_format=_UpperCAmelCase		,    **_UpperCAmelCase   )



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


             '''simple docstring'''




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



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


             '''simple docstring'''




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







       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  ImageInput		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  Dict[str, int] = None		,    _UpperCAmelCase      :  PILImageResampling = None		,    _UpperCAmelCase      :  bool = None		,    _UpperCAmelCase      :  Dict[str, int] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  Optional[float] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  Optional[Union[float, List[float]]] = None		,    _UpperCAmelCase      :  Optional[Union[float, List[float]]] = None		,    _UpperCAmelCase      :  Optional[Union[str, TensorType]] = None		,    _UpperCAmelCase      :  Union[str, ChannelDimension] = ChannelDimension.FIRST		,    **_UpperCAmelCase      :  Optional[int]		,    )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_      			=							size if size is not None else self.size
             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase		,    default_to_square=_UpperCAmelCase   )
             UpperCAmelCase_      			=							resample if resample is not None else self.resample
             UpperCAmelCase_      			=							do_center_crop if do_center_crop is not None else self.do_center_crop
             UpperCAmelCase_      			=							crop_size if crop_size is not None else self.crop_size
             UpperCAmelCase_      			=							get_size_dict(_UpperCAmelCase   )
             UpperCAmelCase_      			=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_      			=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_      			=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_      			=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_      			=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_      			=							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:
                   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."   )

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

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

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

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

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

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

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









"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


lowerCamelCase       	=						logging.get_logger(__name__)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


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


             '''simple docstring'''




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




