





'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase					:		Union[str, Any]		=							{
    '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:
   UpperCAmelCase					:		Dict		=							['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Any		=							[
       '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

   UpperCAmelCase					:		Any		=							_LazyModule(__name__, globals()['__file__'], _import_structure)









'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = 32     ,     lowercase__=PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        return rescale(image=lowercase__     ,     scale=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError('''size_divisor is required for resizing'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              raise ValueError('''Invalid image(s)'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


