



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 							lowerCAmelCase_		:







		def __init__(    self  :   List[Any]       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   Union[str, Any]=sys.maxsize					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	'''bilinear'''
						_UpperCAmelCase						:  Dict     					=	max_size
						_UpperCAmelCase						:  int     					=	short_edge_length







		def __call__(    self  :   Tuple       ,		UpperCAmelCase_  :   Any					)       ->		Optional[int]:


						'''simple docstring'''







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

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

						return img_augs





class 							lowerCAmelCase_		:







		def __init__(    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Optional[Any]					)       ->		Any:


						'''simple docstring'''







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







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   List[Any]					)       ->		Any:


						'''simple docstring'''







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

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







		def __call__(    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   Dict=False					)       ->		List[str]:


						'''simple docstring'''







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

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


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes






def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				assert torch.isfinite(_UpperCamelCase			).all(), "Box tensor contains infinite or NaN!"
				_UpperCAmelCase					, _UpperCAmelCase						:  Optional[Any]     					=	box_size
				tensor[:, 0].clamp_(min=0      ,					max=_UpperCamelCase			)
				tensor[:, 1].clamp_(min=0      ,					max=_UpperCamelCase			)
				tensor[:, 2].clamp_(min=0      ,					max=_UpperCamelCase			)
				tensor[:, 3].clamp_(min=0      ,					max=_UpperCamelCase			)





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__			:   Optional[int]      			=    {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['ErnieMTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   Dict      			=    [
					    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ErnieMForMultipleChoice',
					    'ErnieMForQuestionAnswering',
					    'ErnieMForSequenceClassification',
					    'ErnieMForTokenClassification',
					    'ErnieMModel',
					    'ErnieMPreTrainedModel',
					    'ErnieMForInformationExtraction',
					]


if TYPE_CHECKING:
					from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ernie_m import ErnieMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie_m import (
										    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieMForInformationExtraction,
										    ErnieMForMultipleChoice,
										    ErnieMForQuestionAnswering,
										    ErnieMForSequenceClassification,
										    ErnieMForTokenClassification,
										    ErnieMModel,
										    ErnieMPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__			:   Union[str, Any]      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
