






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 							a_				:


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_=sys.maxsize     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  'bilinear'
														lowerCAmelCase_       		=  max_size
														lowerCAmelCase_       		=  short_edge_length






							def __call__(	self    ,    lowercase_     )      ->      List[Any]:
														'''simple docstring'''

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

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

														return img_augs




class 							a_				:


							'''simple docstring'''






							def __init__(	self    ,    lowercase_     )      ->      Any:
														'''simple docstring'''

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

							def   _lowercase (	self    ,    lowercase_     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  tuple(max(lowercase_     ) for s in zip(*[img.shape for img in images]     )     )
														lowerCAmelCase_       		=  [im.shape[-2:] for im in images]
														lowerCAmelCase_       		=  [
														    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    ,    lowercase_    ,    lowercase_=False     )      ->      Any:
														'''simple docstring'''

														with torch.no_grad():
																					if not isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  [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
																					lowerCAmelCase_       		=  torch.tensor([im.shape[:2] for im in images]     )
																					lowerCAmelCase_       		=  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
																					lowerCAmelCase_       		=  [self.normalizer(lowercase_     ) for x in images]
																					# now pad them to do the following operations
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  self.pad(lowercase_     )
																					# Normalize

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


def   lowerCamelCase      (		a_				,		a_			)			->    Optional[int]:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes



def   lowerCamelCase      (		a_				,		a_			)			->    Tuple:
							assert torch.isfinite(a_			).all(), "Box tensor contains infinite or NaN!"
							lowerCAmelCase_		,     lowerCAmelCase_       		=  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_torch_available


lowerCamelCase_		  =						{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ProphetNetDecoder""",
		    """ProphetNetEncoder""",
		    """ProphetNetForCausalLM""",
		    """ProphetNetForConditionalGeneration""",
		    """ProphetNetModel""",
		    """ProphetNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



