




'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						['''pixel_values''']



   def __init__(   self :     str  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     int = 0.9  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BICUBIC  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     Union[int, float] = 1 / 2_5_5  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				**__lowerCamelCase :     Optional[int]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				size if size is not None else {"shortest_edge": 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				param_name="crop_size"   )

      _SCREAMING_SNAKE_CASE	=				do_resize
      _SCREAMING_SNAKE_CASE	=				size
      _SCREAMING_SNAKE_CASE	=				crop_pct
      _SCREAMING_SNAKE_CASE	=				resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size
      _SCREAMING_SNAKE_CASE	=				do_rescale
      _SCREAMING_SNAKE_CASE	=				rescale_factor
      _SCREAMING_SNAKE_CASE	=				do_normalize
      _SCREAMING_SNAKE_CASE	=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _SCREAMING_SNAKE_CASE	=				image_std if image_std is not None else IMAGENET_DEFAULT_STD



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     Optional[float] = None  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BICUBIC  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     str  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""   )
      if crop_pct is not None:
         if "shortest_edge" in size:
            _SCREAMING_SNAKE_CASE	=				int(size["shortest_edge"] / crop_pct   )
         elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
               _SCREAMING_SNAKE_CASE	=				int(size["height"] / crop_pct   )
            else:
               _SCREAMING_SNAKE_CASE	=				(int(size["height"] / crop_pct   ), int(size["width"] / crop_pct   ))
         else:
            raise ValueError("Invalid size for resize: {}".format(__lowerCamelCase   )   )

         _SCREAMING_SNAKE_CASE	=				get_resize_output_image_size(__lowerCamelCase  ,				size=__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      else:
         if "shortest_edge" in size:
            _SCREAMING_SNAKE_CASE	=				get_resize_output_image_size(__lowerCamelCase  ,				size=size["shortest_edge"]  ,				default_to_square=__lowerCamelCase   )
         elif "height" in size and "width" in size:
            _SCREAMING_SNAKE_CASE	=				(size["height"], size["width"])
         else:
            raise ValueError("Invalid size for resize: {}".format(__lowerCamelCase   )   )

      return resize(__lowerCamelCase  ,				size=__lowerCamelCase  ,				resample=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Optional[int]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""   )
      return center_crop(__lowerCamelCase  ,				size=(size["height"], size["width"])  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Union[int, float]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      return rescale(__lowerCamelCase  ,				scale=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      return normalize(__lowerCamelCase  ,				mean=__lowerCamelCase  ,				std=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     ImageInput  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     int = None  ,				__lowerCamelCase :     PILImageResampling = None  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     float = None  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[str, TensorType]] = None  ,				__lowerCamelCase :     ChannelDimension = ChannelDimension.FIRST  ,				**__lowerCamelCase :     Tuple  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				do_resize if do_resize is not None else self.do_resize
      _SCREAMING_SNAKE_CASE	=				crop_pct if crop_pct is not None else self.crop_pct
      _SCREAMING_SNAKE_CASE	=				resample if resample is not None else self.resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop if do_center_crop is not None else self.do_center_crop
      _SCREAMING_SNAKE_CASE	=				do_rescale if do_rescale is not None else self.do_rescale
      _SCREAMING_SNAKE_CASE	=				rescale_factor if rescale_factor is not None else self.rescale_factor
      _SCREAMING_SNAKE_CASE	=				do_normalize if do_normalize is not None else self.do_normalize
      _SCREAMING_SNAKE_CASE	=				image_mean if image_mean is not None else self.image_mean
      _SCREAMING_SNAKE_CASE	=				image_std if image_std is not None else self.image_std

      _SCREAMING_SNAKE_CASE	=				size if size is not None else self.size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else self.crop_size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				param_name="crop_size"   )

      _SCREAMING_SNAKE_CASE	=				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 or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."   )

      if do_center_crop and crop_pct is None:
         raise ValueError("Crop_pct 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.
      _SCREAMING_SNAKE_CASE	=				[to_numpy_array(__lowerCamelCase   ) for image in images]

      if do_resize:
         _SCREAMING_SNAKE_CASE	=				[self.resize(image=__lowerCamelCase  ,				size=__lowerCamelCase  ,				crop_pct=__lowerCamelCase  ,				resample=__lowerCamelCase   ) for image in images]

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

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

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

      _SCREAMING_SNAKE_CASE	=				[to_channel_dimension_format(__lowerCamelCase  ,				__lowerCamelCase   ) for image in images]

      _SCREAMING_SNAKE_CASE	=				{"pixel_values": images}
      return BatchFeature(data=__lowerCamelCase  ,				tensor_type=__lowerCamelCase   )







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_			=						{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure)

