






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 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[Any]=sys.maxsize   ):
       SCREAMING_SNAKE_CASE							:   str						    =     "bilinear"
       SCREAMING_SNAKE_CASE							:   str						    =     max_size
       SCREAMING_SNAKE_CASE							:   int						    =     short_edge_length






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

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

       return img_augs





class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


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


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     tuple(max(UpperCAmelCase_   ) for s in zip(*[img.shape for img in images]   )   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     [im.shape[-2:] for im in images]
       SCREAMING_SNAKE_CASE							:   str						    =     [
           nn.functional.pad(
               UpperCAmelCase_       ,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,			value=self.pad_value       ,			)
           for size, im in zip(UpperCAmelCase_       ,			UpperCAmelCase_   )
       ]

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






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

         if self.size_divisibility > 0:
           raise NotImplementedError()
         # pad
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.true_divide(UpperCAmelCase_       ,			UpperCAmelCase_   )
         if single_image:
           return images[0], sizes[0], scales_yx[0]
         else:
           return images, sizes, scales_yx





def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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










import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							str	   =       'AutoImageProcessor'
     __lowercase				:							Dict	   =       'AutoTokenizer'

     def __init__(					self:int     ,		_a:List[str]=None     ,		_a:Optional[Any]=None     ,		**_a:List[str]   ):
       snake_case__						= None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,		_a     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''   )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''   )

       super().__init__(_a     ,		_a   )
       snake_case__						= self.image_processor
       snake_case__						= False

     def __call__(					self:Optional[int]     ,		*_a:str     ,		**_a:int   ):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*_a     ,		**_a   )

       snake_case__						= kwargs.pop('''images'''     ,		_a   )
       snake_case__						= kwargs.pop('''text'''     ,		_a   )
       if len(_a   ) > 0:
         snake_case__						= args[0]
         snake_case__						= args[1:]

       if images is None and text is None:
         raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

       if images is not None:
         snake_case__						= self.image_processor(_a     ,		*_a     ,		**_a   )
       if text is not None:
         snake_case__						= self.tokenizer(_a     ,		**_a   )

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         snake_case__						= encodings['''input_ids''']
         return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Union[str, Any]     ,		**_a:Any   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		*_a:Union[str, Any]     ,		**_a:Optional[int]   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @contextmanager
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your images inputs, or in a separate call.'''   )
       snake_case__						= True
       snake_case__						= self.tokenizer
       yield
       snake_case__						= self.image_processor
       snake_case__						= False

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict     ,		_a:Dict=False     ,		_a:Optional[int]=None   ):
       if added_vocab is None:
         snake_case__						= self.tokenizer.get_added_vocab()

       snake_case__						= {}

       while tokens:
         snake_case__						= re.search(r'''<s_(.*?)>'''     ,		_a     ,		re.IGNORECASE   )
         if start_token is None:
           break
         snake_case__						= start_token.group(1   )
         snake_case__						= re.search(rF"""</s_{key}>"""     ,		_a     ,		re.IGNORECASE   )
         snake_case__						= start_token.group()
         if end_token is None:
           snake_case__						= tokens.replace(_a     ,		''''''   )
         else:
           snake_case__						= end_token.group()
           snake_case__						= re.escape(_a   )
           snake_case__						= re.escape(_a   )
           snake_case__						= re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""     ,		_a     ,		re.IGNORECASE   )
           if content is not None:
             snake_case__						= content.group(1   ).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               snake_case__						= self.tokenajson(_a     ,		is_inner_value=_a     ,		added_vocab=_a   )
               if value:
                 if len(_a   ) == 1:
                   snake_case__						= value[0]
                 snake_case__						= value
             else:  # leaf nodes
               snake_case__						= []
               for leaf in content.split(r'''<sep/>'''   ):
                 snake_case__						= leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   snake_case__						= leaf[1:-2]  # for categorical special tokens
                 output[key].append(_a   )
               if len(output[key]   ) == 1:
                 snake_case__						= output[key][0]

           snake_case__						= tokens[tokens.find(_a   ) + len(_a   ) :].strip()
           if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:]     ,		is_inner_value=_a     ,		added_vocab=_a   )

       if len(_a   ):
         return [output] if is_inner_value else output
       else:
         return [] if is_inner_value else {"text_sequence": tokens}

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		_a     ,		)
       return self.image_processor_class







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor

