



'''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    lowerCamelCase_				:






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=sys.maxsize			):
       UpperCamelCase__	     =      "bilinear"
       UpperCamelCase__	     =      max_size
       UpperCamelCase__	     =      short_edge_length







   def __call__(	self  ,    __UpperCamelCase			):
       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(lowerCAmelCase__  ,    lowerCAmelCase__			)
           if h < w:
               UpperCamelCase__     ,	UpperCamelCase__	     =      size, scale * w
           else:
               UpperCamelCase__     ,	UpperCamelCase__	     =      scale * h, size
           if max(lowerCAmelCase__  ,    lowerCAmelCase__			) > self.max_size:
               UpperCamelCase__	     =      self.max_size * 1.0 / max(lowerCAmelCase__  ,    lowerCAmelCase__			)
               UpperCamelCase__	     =      newh * scale
               UpperCamelCase__	     =      neww * scale
           UpperCamelCase__	     =      int(neww + 0.5			)
           UpperCamelCase__	     =      int(newh + 0.5			)

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

       return img_augs

class    lowerCamelCase_				:






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase			):
       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 __UpperCamelCase			: (x - self.pixel_mean) / self.pixel_std


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

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







   def __call__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=False			):
       with torch.no_grad():
           if not isinstance(lowerCAmelCase__  ,    lowerCAmelCase__			):
               UpperCamelCase__	     =      [images]
           if single_image:
               assert len(lowerCAmelCase__			) == 1
           for i in range(len(lowerCAmelCase__			)			):
               if isinstance(images[i]  ,    torch.Tensor			):
                   images.insert(lowerCAmelCase__  ,    images.pop(lowerCAmelCase__			).to(self.device			).float()			)
               elif not isinstance(images[i]  ,    torch.Tensor			):
                   images.insert(
                       lowerCAmelCase__  ,    torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase__			)  ,    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(lowerCAmelCase__			)
           # 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(lowerCAmelCase__			) for x in images]
           # now pad them to do the following operations
           UpperCamelCase__     ,	UpperCamelCase__	     =      self.pad(lowerCAmelCase__			)
           # Normalize

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





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    assert torch.isfinite(lowerCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
    UpperCamelCase__     ,	UpperCamelCase__	     =      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

