






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


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





     """simple docstring"""






     a_     =		["pixel_values"]



     def __init__( self					:  Any							,		__A					:  bool = True							,		__A					:  int = 3_2							,		__A					:  Tuple=PILImageResampling.BILINEAR							,		__A					:  bool = True							,		**__A					:  Optional[Any]							,		):
      snake_case__			:				List[str]       =  do_resize
      snake_case__			:				Optional[Any]       =  do_rescale
      snake_case__			:				int       =  size_divisor
      snake_case__			:				int       =  resample
      super().__init__(**__A	)



     def 	_lowercase					( self					:  List[str]							,		__A					:  np.ndarray							,		__A					:  int							,		__A					:  Union[str, Any]							,		__A					:  Optional[ChannelDimension] = None							,		**__A					:  Any	):
      snake_case__			:				Optional[Any]       =  get_image_size(__A	)
      # Rounds the height and width down to the closest multiple of size_divisor
      snake_case__			:				Optional[int]       =  height // size_divisor * size_divisor
      snake_case__			:				Any       =  width // size_divisor * size_divisor
      snake_case__			:				Optional[Any]       =  resize(__A							,		(new_h, new_w)							,		resample=__A							,		data_format=__A							,		**__A	)
      return image



     def 	_lowercase					( self					:  Any							,		__A					:  np.ndarray							,		__A					:  float							,		__A					:  Optional[ChannelDimension] = None							,		**__A					:  List[str]	):
      return rescale(image=__A							,		scale=__A							,		data_format=__A							,		**__A	)






     def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]							,		__A					:  Optional[bool] = None							,		__A					:  Optional[int] = None							,		__A					:  Dict=None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[TensorType, str]] = None							,		__A					:  ChannelDimension = ChannelDimension.FIRST							,		**__A					:  Tuple							,		):
      snake_case__			:				str       =  do_resize if do_resize is not None else self.do_resize
      snake_case__			:				Union[str, 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__			:				List[Any]       =  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__			:				Dict       =  make_list_of_images(__A	)

      if not valid_images(__A	):
       raise ValueError("Invalid image(s)"	)

      # All transformations expect numpy arrays.
      snake_case__			:				int       =  [to_numpy_array(__A	) for img in images]

      if do_resize:
       snake_case__			:				Tuple       =  [self.resize(__A							,		size_divisor=__A							,		resample=__A	) for image in images]

      if do_rescale:
       snake_case__			:				str       =  [self.rescale(__A							,		scale=1 / 2_5_5	) for image in images]

      snake_case__			:				Dict       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

      snake_case__			:				int       =  {"pixel_values": images}
      return BatchFeature(data=__A							,		tensor_type=__A	)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """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:
	__lowerCamelCase :      List[Any]										=				[
	    """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

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



