

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   __UpperCamelCase      :




						def __init__(						self		:  List[str]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Dict=sys.maxsize							):


							'''simple docstring'''







							UpperCAmelCase_    			=     "bilinear"
							UpperCAmelCase_    			=     max_size
							UpperCAmelCase_    			=     short_edge_length




						def __call__(						self		:  List[Any]						,		lowerCAmelCase		:  Optional[int]							):


							'''simple docstring'''







							UpperCAmelCase_    			=     []
							for img in imgs:
								UpperCAmelCase_    			=     img.shape[:2]
								# later: provide list and randomly choose index for resize
								UpperCAmelCase_    			=     np.random.randint(self.short_edge_length[0]						,		self.short_edge_length[1] + 1							)
								if size == 0:
									return img
								UpperCAmelCase_    			=     size * 1.0 / min(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
								if h < w:
									UpperCAmelCase_    			=     size, scale * w
								else:
									UpperCAmelCase_    			=     scale * h, size
								if max(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							) > self.max_size:
									UpperCAmelCase_    			=     self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
									UpperCAmelCase_    			=     newh * scale
									UpperCAmelCase_    			=     neww * scale
								UpperCAmelCase_    			=     int(neww + 0.5							)
								UpperCAmelCase_    			=     int(newh + 0.5							)

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

							return img_augs


class   __UpperCamelCase      :




						def __init__(						self		:  str						,		lowerCAmelCase		:  List[str]							):


							'''simple docstring'''







							UpperCAmelCase_    			=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,		cfg.INPUT.MAX_SIZE_TEST							)
							UpperCAmelCase_    			=     cfg.INPUT.FORMAT
							UpperCAmelCase_    			=     cfg.SIZE_DIVISIBILITY
							UpperCAmelCase_    			=     cfg.PAD_VALUE
							UpperCAmelCase_    			=     cfg.INPUT.MAX_SIZE_TEST
							UpperCAmelCase_    			=     cfg.MODEL.DEVICE
							UpperCAmelCase_    			=     torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)						,		1						,		1							)
							UpperCAmelCase_    			=     torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)						,		1						,		1							)
							UpperCAmelCase_    			=     lambda lowerCAmelCase							: (x - self.pixel_mean) / self.pixel_std




						def  __A   (						self		:  List[str]						,		lowerCAmelCase		:  Optional[int]							):


							'''simple docstring'''







							UpperCAmelCase_    			=     tuple(max(SCREAMING_SNAKE_CASE__							) for s in zip(*[img.shape for img in images]							)							)
							UpperCAmelCase_    			=     [im.shape[-2:] for im in images]
							UpperCAmelCase_    			=     [
							    nn.functional.pad(
							        SCREAMING_SNAKE_CASE__						,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,		value=self.pad_value						,		)
							    for size, im in zip(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
							]

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




						def __call__(						self		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Tuple=False							):


							'''simple docstring'''







							with torch.no_grad():
								if not isinstance(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							):
									UpperCAmelCase_    			=     [images]
								if single_image:
									assert len(SCREAMING_SNAKE_CASE__							) == 1
								for i in range(len(SCREAMING_SNAKE_CASE__							)							):
									if isinstance(images[i]						,		torch.Tensor							):
										images.insert(SCREAMING_SNAKE_CASE__						,		images.pop(SCREAMING_SNAKE_CASE__							).to(self.device							).float()							)
									elif not isinstance(images[i]						,		torch.Tensor							):
										images.insert(
										    SCREAMING_SNAKE_CASE__						,		torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE__							)						,		input_format=self.input_format							)							)
										    .to(self.device							)
										    .float()						,		)
										 # resize smallest edge
								UpperCAmelCase_    			=     torch.tensor([im.shape[:2] for im in images]							)
								UpperCAmelCase_    			=     self.aug(SCREAMING_SNAKE_CASE__							)
								# 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
								UpperCAmelCase_    			=     [self.normalizer(SCREAMING_SNAKE_CASE__							) for x in images]
								# now pad them to do the following operations
								UpperCAmelCase_    			=     self.pad(SCREAMING_SNAKE_CASE__							)
								# Normalize

								if self.size_divisibility > 0:
									raise NotImplementedError()
								# pad
								UpperCAmelCase_    			=     torch.true_divide(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
								if single_image:
									return images[0], sizes[0], scales_yx[0]
								else:
									return images, sizes, scales_yx



def  __lowerCAmelCase (  A    ,       A      ):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes




def  __lowerCAmelCase (  A    ,       A      ):
	assert torch.isfinite(A      ).all(), "Box tensor contains infinite or NaN!"
	UpperCAmelCase_    			=     box_size
	tensor[:, 0].clamp_(min=0    ,       max=A      )
	tensor[:, 1].clamp_(min=0    ,       max=A      )
	tensor[:, 2].clamp_(min=0    ,       max=A      )
	tensor[:, 3].clamp_(min=0    ,       max=A      )


from typing import TYPE_CHECKING

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


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

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