



"""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   a__		:


   def __init__(							self      :							Tuple,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							List[Any]=sys.maxsize			)    ->       int:
         lowercase  :      str					=   'bilinear'
         lowercase  :      Union[str, Any]					=   max_size
         lowercase  :      Dict					=   short_edge_length






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

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

         return img_augs




class   a__		:


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


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

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




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


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





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


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





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

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

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         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 audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
