
"""simple docstring"""





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    ,		__a    ,		__a=sys.maxsize)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  '''bilinear'''
							_UpperCamelCase										=  max_size
							_UpperCamelCase										=  short_edge_length




		def __call__( self    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  []
							for img in imgs:
												_UpperCamelCase			,  _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(__a    ,		__a)
												if h < w:
																	_UpperCamelCase			,  _UpperCamelCase										=  size, scale * w
												else:
																	_UpperCamelCase			,  _UpperCamelCase										=  scale * h, size
												if max(__a    ,		__a) > self.max_size:
																	_UpperCamelCase										=  self.max_size * 1.0 / max(__a    ,		__a)
																	_UpperCamelCase										=  newh * scale
																	_UpperCamelCase										=  neww * scale
												_UpperCamelCase										=  int(neww + 0.5)
												_UpperCamelCase										=  int(newh + 0.5)

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

							return img_augs






class    _UpperCAmelCase:



		def __init__( self    ,		__a)	->	int:






							'''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 __a: (x - self.pixel_mean) / self.pixel_std



		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






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

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




		def __call__( self    ,		__a    ,		__a=False)	->	Any:






							'''simple docstring'''
							with torch.no_grad():
												if not isinstance(__a    ,		__a):
																	_UpperCamelCase										=  [images]
												if single_image:
																	assert len(__a) == 1
												for i in range(len(__a)):
																	if isinstance(images[i]    ,		torch.Tensor):
																						images.insert(__a    ,		images.pop(__a).to(self.device).float())
																	elif not isinstance(images[i]    ,		torch.Tensor):
																						images.insert(
																						    __a    ,		torch.as_tensor(img_tensorize(images.pop(__a)    ,		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(__a)
												# 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(__a) for x in images]
												# now pad them to do the following operations
												_UpperCamelCase			,  _UpperCamelCase										=  self.pad(__a)
												# Normalize

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





def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Any:


					"""simple docstring"""





					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Union[str, Any]:


					"""simple docstring"""





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








"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



		def __init__( self    ,		__a=4_23_84    ,		__a=10_24    ,		__a=24    ,		__a=16    ,		__a=40_96    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10_24    ,		__a=0.02    ,		__a=1e-12    ,		__a=True    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






