




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      ,snake_case__      ,snake_case__=sys.maxsize ):
							SCREAMING_SNAKE_CASE_   : str					  =      'bilinear'
							SCREAMING_SNAKE_CASE_   : List[str]					  =      max_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      short_edge_length






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

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

							return img_augs







class 				lowerCAmelCase_       :






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






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      tuple(max(snake_case__ ) for s in zip(*[img.shape for img in images] ) )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [im.shape[-2:] for im in images]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [
							    nn.functional.pad(
							        snake_case__      ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,value=self.pad_value      ,)
							    for size, im in zip(snake_case__      ,snake_case__ )
							]

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






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

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     str  )       ->  Any:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Tuple[int, int]  )       ->  List[Any]:







			"""simple docstring"""





			assert torch.isfinite(lowerCamelCase_  ).all(), "Box tensor contains infinite or NaN!"
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							None
		_a       					=							None
		_a       					=							None
		_a       					=							None



class   lowercase      (				A__   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=1       ,  UpperCamelCase_=0       ,  UpperCamelCase_=2       ,  UpperCamelCase_=512       ,  UpperCamelCase_="cls"       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_       ,  bos_token_id=UpperCamelCase_       ,  eos_token_id=UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					project_dim
							UpperCamelCase__  :List[str]				   =					pooler_fn
							UpperCamelCase__  :Dict				   =					learn_encoder
							UpperCamelCase__  :Any				   =					use_attention_mask



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							[R'pooler', R'logit_scale']
		_a       					=							[R'position_ids', R'predictions.decoder.bias']
		_a       					=							'roberta'
		_a       					=							RobertaSeriesConfig




		def __init__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							super().__init__(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel(UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
							UpperCamelCase__  :Union[str, Any]				   =					getattr(UpperCamelCase_       ,  '''has_pre_transformation'''       ,  UpperCamelCase_				)
							if self.has_pre_transformation:
												UpperCamelCase__  :Any				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
												UpperCamelCase__  :str				   =					nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps				)
							self.post_init()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase__  :str				   =					self.base_model(
							    input_ids=UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  position_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_       ,  inputs_embeds=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  output_attentions=UpperCamelCase_       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=UpperCamelCase_       ,  )

							if self.has_pre_transformation:
												UpperCamelCase__  :Dict				   =					outputs['''hidden_states'''][-2]
												UpperCamelCase__  :Optional[int]				   =					self.pre_LN(UpperCamelCase_				)
												UpperCamelCase__  :Optional[Any]				   =					self.transformation_pre(UpperCamelCase_				)

												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
							else:
												UpperCamelCase__  :List[Any]				   =					self.transformation(outputs.last_hidden_state				)
												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )