





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=sys.maxsize       ):
									__a	:  Tuple         =					'''bilinear'''
									__a	:  Tuple         =					max_size
									__a	:  Optional[Any]         =					short_edge_length







					def __call__(  self		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					[]
									for img in imgs:
													__a	:  Optional[Any]         =					img.shape[:2]
													# later: provide list and randomly choose index for resize
													__a	:  Dict         =					np.random.randint(self.short_edge_length[0]		,     self.short_edge_length[1] + 1       )
													if size == 0:
																	return img
													__a	:  Any         =					size * 1.0 / min(__lowercase		,     __lowercase       )
													if h < w:
																	__a	:  Optional[int]         =					size, scale * w
													else:
																	__a	:  List[Any]         =					scale * h, size
													if max(__lowercase		,     __lowercase       ) > self.max_size:
																	__a	:  str         =					self.max_size * 1.0 / max(__lowercase		,     __lowercase       )
																	__a	:  Tuple         =					newh * scale
																	__a	:  Optional[Any]         =					neww * scale
													__a	:  List[str]         =					int(neww + 0.5       )
													__a	:  int         =					int(newh + 0.5       )

													if img.dtype == np.uinta:
																	__a	:  List[str]         =					Image.fromarray(__lowercase       )
																	__a	:  str         =					pil_image.resize((neww, newh)		,     PILImageResampling.BILINEAR       )
																	__a	:  Any         =					np.asarray(__lowercase       )
													else:
																	__a	:  List[Any]         =					img.permute(2		,     0		,     1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
																	__a	:  List[str]         =					nn.functional.interpolate(
																	    __lowercase		,     (newh, neww)		,     mode=self.interp_method		,     align_corners=__lowercase       ).squeeze(0       )
													img_augs.append(__lowercase       )

									return img_augs






class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,     cfg.INPUT.MAX_SIZE_TEST       )
									__a	:  Union[str, Any]         =					cfg.INPUT.FORMAT
									__a	:  Optional[Any]         =					cfg.SIZE_DIVISIBILITY
									__a	:  List[Any]         =					cfg.PAD_VALUE
									__a	:  Optional[Any]         =					cfg.INPUT.MAX_SIZE_TEST
									__a	:  Tuple         =					cfg.MODEL.DEVICE
									__a	:  List[Any]         =					torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,     1		,     1       )
									__a	:  List[str]         =					torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,     1		,     1       )
									__a	:  Any         =					lambda _UpperCAmelCase       : (x - self.pixel_mean) / self.pixel_std







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Tuple         =					tuple(max(__lowercase       ) for s in zip(*[img.shape for img in images]       )       )
									__a	:  str         =					[im.shape[-2:] for im in images]
									__a	:  Optional[int]         =					[
									    nn.functional.pad(
									        __lowercase		,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,     value=self.pad_value		,     )
									    for size, im in zip(__lowercase		,     __lowercase       )
									]

									return torch.stack(__lowercase       ), torch.tensor(__lowercase       )







					def __call__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=False       ):
									with torch.no_grad():
													if not isinstance(__lowercase		,     __lowercase       ):
																	__a	:  str         =					[images]
													if single_image:
																	assert len(__lowercase       ) == 1
													for i in range(len(__lowercase       )       ):
																	if isinstance(images[i]		,     torch.Tensor       ):
																					images.insert(__lowercase		,     images.pop(__lowercase       ).to(self.device       ).float()       )
																	elif not isinstance(images[i]		,     torch.Tensor       ):
																					images.insert(
																					    __lowercase		,     torch.as_tensor(img_tensorize(images.pop(__lowercase       )		,     input_format=self.input_format       )       )
																					    .to(self.device       )
																					    .float()		,     )
            # resize smallest edge
													__a	:  Union[str, Any]         =					torch.tensor([im.shape[:2] for im in images]       )
													__a	:  List[str]         =					self.aug(__lowercase       )
													# transpose images and convert to torch tensors
													# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
													# now normalize before pad to avoid useless arithmetic
													__a	:  int         =					[self.normalizer(__lowercase       ) for x in images]
													# now pad them to do the following operations
													__a	:  List[Any]         =					self.pad(__lowercase       )
													# Normalize

													if self.size_divisibility > 0:
																	raise NotImplementedError()
													# pad
													__a	:  Union[str, Any]         =					torch.true_divide(__lowercase		,     __lowercase       )
													if single_image:
																	return images[0], sizes[0], scales_yx[0]
													else:
																	return images, sizes, scales_yx






def       __A (     a_  :List[Any]				,    a_  :Any)						->						List[str]:
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes






def       __A (     a_  :List[str]				,    a_  :Union[str, Any])						->						Dict:
				assert torch.isfinite(__lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
				__a	:  Any         =					box_size
				tensor[:, 0].clamp_(min=0				,    max=__lowerCAmelCase)
				tensor[:, 1].clamp_(min=0				,    max=__lowerCAmelCase)
				tensor[:, 2].clamp_(min=0				,    max=__lowerCAmelCase)
				tensor[:, 3].clamp_(min=0				,    max=__lowerCAmelCase)






"""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__)