



'''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_       :
    """simple docstring"""







    def __init__(    self					:     List[str] ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[Any]=sys.maxsize						):
          snake_case__  :    List[str]    						=      """bilinear"""
          snake_case__  :    Tuple    						=      max_size
          snake_case__  :    Union[str, Any]    						=      short_edge_length




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

                if img.dtype == np.uinta:
                      snake_case__  :    Dict    						=      Image.fromarray(snake_case_						)
                      snake_case__  :    str    						=      pil_image.resize((neww, newh) ,     PILImageResampling.BILINEAR						)
                      snake_case__  :    str    						=      np.asarray(snake_case_						)
                else:
                      snake_case__  :    List[str]    						=      img.permute(2 ,     0 ,     1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
                      snake_case__  :    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     UpperCAmelCase_       :
    """simple docstring"""







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




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      tuple(max(snake_case_						) for s in zip(*[img.shape for img in images]						)						)
          snake_case__  :    Dict    						=      [im.shape[-2:] for im in images]
          snake_case__  :    str    						=      [
              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					:     Union[str, Any] ,     snake_case_					:     List[str] ,     snake_case_					:     List[Any]=False						):
          with torch.no_grad():
                if not isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    List[Any]    						=      [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
                snake_case__  :    Any    						=      torch.tensor([im.shape[:2] for im in images]						)
                snake_case__  :    Union[str, Any]    						=      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
                snake_case__  :    Optional[Any]    						=      [self.normalizer(snake_case_						) for x in images]
                # now pad them to do the following operations
                snake_case__						,				snake_case__  :    Optional[int]    						=      self.pad(snake_case_						)
                # Normalize

                if self.size_divisibility > 0:
                      raise NotImplementedError()
                # pad
                snake_case__  :    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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      assert torch.isfinite(_lowerCAmelCase		).all(), "Box tensor contains infinite or NaN!"
      snake_case__						,				snake_case__  :    Any    						=      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		)



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



