





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


snake_case_							=			logging.get_logger(__name__)
class        snake_case_		(			_A):
		lowerCamelCase    :Optional[Any]						=     ["pixel_values"]







		def __init__(		self			,     __lowercase = True			,     __lowercase = 1 / 2_5_5			,     __lowercase = True			,     __lowercase = 8			,     **__lowercase			,     )				->   None:
				super().__init__(**__lowercase   )

				lowerCamelCase   : Optional[Any]								=do_rescale
				lowerCamelCase   : Any								=rescale_factor
				lowerCamelCase   : Optional[Any]								=do_pad
				lowerCamelCase   : Optional[Any]								=pad_size







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase = None			,     **__lowercase   )				->   np.ndarray:
				return rescale(__lowercase			,     scale=__lowercase			,     data_format=__lowercase			,     **__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase = None   )				->   List[Any]:
				lowerCamelCase   : List[str]								=get_image_size(__lowercase   )
				lowerCamelCase   : List[str]								=(old_height // size + 1) * size - old_height
				lowerCamelCase   : Optional[Any]								=(old_width // size + 1) * size - old_width

				return pad(__lowercase			,     ((0, pad_height), (0, pad_width))			,     mode='''symmetric'''			,     data_format=__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = ChannelDimension.FIRST			,     **__lowercase			,     )				->   Union[str, Any]:
				lowerCamelCase   : Dict								=do_rescale if do_rescale is not None else self.do_rescale
				lowerCamelCase   : Optional[int]								=rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCamelCase   : List[Any]								=do_pad if do_pad is not None else self.do_pad
				lowerCamelCase   : str								=pad_size if pad_size is not None else self.pad_size

				lowerCamelCase   : Optional[Any]								=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_rescale and rescale_factor is None:
						raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

				# All transformations expect numpy arrays.
				lowerCamelCase   : int								=[to_numpy_array(__lowercase   ) for image in images]

				if do_rescale:
						lowerCamelCase   : List[Any]								=[self.rescale(image=__lowercase			,     scale=__lowercase   ) for image in images]

				if do_pad:
						lowerCamelCase   : str								=[self.pad(__lowercase			,     size=__lowercase   ) for image in images]

				lowerCamelCase   : str								=[to_channel_dimension_format(__lowercase			,     __lowercase   ) for image in images]

				lowerCamelCase   : Union[str, Any]								={'''pixel_values''': images}
				return BatchFeature(data=__lowercase			,     tensor_type=__lowercase   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_							=			{
    '''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:
				snake_case_							=			[
				    '''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

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

