






'''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


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
      snake_case_												=							["""pixel_values"""]




      def __init__( self		:							Tuple   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							Dict   , )		->  None:
            super().__init__(**__lowercase )
            SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else {'''shortest_edge''': 2_56}
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
            SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
            SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=do_resize
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=size
            SCREAMING_SNAKE_CASE__       :			List[Any]													=resample
            SCREAMING_SNAKE_CASE__       :			Any													=do_center_crop
            SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size
            SCREAMING_SNAKE_CASE__       :			int													=do_rescale
            SCREAMING_SNAKE_CASE__       :			int													=rescale_factor
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=do_normalize
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            SCREAMING_SNAKE_CASE__       :			List[str]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




      def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[int]   , )		->  np.ndarray:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
            if "shortest_edge" not in size:
                  raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
            SCREAMING_SNAKE_CASE__       :			Any													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
            return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




      def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Union[str, Any]   , )		->  np.ndarray:
            SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase )
            return center_crop(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




      def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Union[str, Any] )		->  np.ndarray:
            return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




      def        __magic_name__		( self		:							Tuple   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[int]   , )		->  np.ndarray:
            return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




      def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  int:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=do_resize if do_resize is not None else self.do_resize
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=size if size is not None else self.size
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=resample if resample is not None else self.resample
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
            SCREAMING_SNAKE_CASE__       :			Tuple													=crop_size if crop_size is not None else self.crop_size
            SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase )
            SCREAMING_SNAKE_CASE__       :			Dict													=do_rescale if do_rescale is not None else self.do_rescale
            SCREAMING_SNAKE_CASE__       :			Dict													=rescale_factor if rescale_factor is not None else self.rescale_factor
            SCREAMING_SNAKE_CASE__       :			Any													=do_normalize if do_normalize is not None else self.do_normalize
            SCREAMING_SNAKE_CASE__       :			Dict													=image_mean if image_mean is not None else self.image_mean
            SCREAMING_SNAKE_CASE__       :			List[Any]													=image_std if image_std is not None else self.image_std

            SCREAMING_SNAKE_CASE__       :			Tuple													=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                  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.
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[to_numpy_array(__lowercase ) for image in images]

            if do_resize:
                  SCREAMING_SNAKE_CASE__       :			int													=[self.resize(image=__lowercase   , size=__lowercase   , resample=__lowercase ) for image in images]

            if do_center_crop:
                  SCREAMING_SNAKE_CASE__       :			Any													=[self.center_crop(image=__lowercase   , size=__lowercase ) for image in images]

            if do_rescale:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

            if do_normalize:
                  SCREAMING_SNAKE_CASE__       :			int													=[self.normalize(image=__lowercase   , mean=__lowercase   , std=__lowercase ) for image in images]

            SCREAMING_SNAKE_CASE__       :			Any													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

            SCREAMING_SNAKE_CASE__       :			Tuple													={'''pixel_values''': images}
            return BatchFeature(data=__lowercase   , tensor_type=__lowercase )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InstructBlipQFormerModel',
      'InstructBlipPreTrainedModel',
      'InstructBlipForConditionalGeneration',
      'InstructBlipVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_instructblip import (
      INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      InstructBlipConfig,
      InstructBlipQFormerConfig,
      InstructBlipVisionConfig,
  )
  from .processing_instructblip import InstructBlipProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_instructblip import (
        INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        InstructBlipForConditionalGeneration,
        InstructBlipPreTrainedModel,
        InstructBlipQFormerModel,
        InstructBlipVisionModel,
    )

else:
  import sys

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