





"""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,
    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


a_			=      logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					["""pixel_values"""]

 def __init__(   self ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = PILImageResampling.BICUBIC ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						__lowerCamelCase = 1 / 255 ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )
     __A   :						Optional[Any]									=					size if size is not None else {'''shortest_edge''': 224}
     __A   :						Dict									=					get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase  )
     __A   :						str									=					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A   :						str									=					get_size_dict(__lowerCamelCase ,						default_to_square=__lowerCamelCase ,						param_name='''crop_size'''  )

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = PILImageResampling.BICUBIC ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					get_size_dict(__lowerCamelCase  )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
     return center_crop(__lowerCamelCase ,						size=(size['''height'''], size['''width''']) ,						data_format=__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

     __A   :						Optional[int]									=					make_list_of_images(__lowerCamelCase  )

     if not valid_images(__lowerCamelCase  ):
         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:
         __A   :						int									=					[convert_to_rgb(__lowerCamelCase  ) for image in images]

     # All transformations expect numpy arrays.
     __A   :						List[str]									=					[to_numpy_array(__lowerCamelCase  ) for image in images]

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

     if do_center_crop:
         __A   :						List[Any]									=					[self.center_crop(image=__lowerCamelCase ,						size=__lowerCamelCase  ) for image in images]

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

     if do_normalize:
         __A   :						Union[str, Any]									=					[self.normalize(image=__lowerCamelCase ,						mean=__lowerCamelCase ,						std=__lowerCamelCase  ) for image in images]

     __A   :						Optional[int]									=					[to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase  ) for image in images]

     __A   :						int									=					{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase  )











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




