


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       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      List[Any]=sys.maxsize	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  "bilinear"
             lowerCamelCase__		:    List[str]   =  max_size
             lowerCamelCase__		:    Optional[int]   =  short_edge_length





       def __call__(       self							:      List[Any] ,				__lowerCamelCase							:      Dict	):


             '''simple docstring'''



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

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

             return img_augs







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      List[str] ,				__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      int ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  tuple(max(__lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
             lowerCamelCase__		:    Union[str, Any]   =  [im.shape[-2:] for im in images]
             lowerCamelCase__		:    Any   =  [
                 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							:      Any ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Any=False	):


             '''simple docstring'''



             with torch.no_grad():
                   if not isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                         lowerCamelCase__		:    List[Any]   =  [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
                   lowerCamelCase__		:    Union[str, Any]   =  torch.tensor([im.shape[:2] for im in images]	)
                   lowerCamelCase__		:    List[str]   =  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
                   lowerCamelCase__		:    Tuple   =  [self.normalizer(__lowerCamelCase	) for x in images]
                   # now pad them to do the following operations
                   lowerCamelCase__		:    Union[str, Any]   =  self.pad(__lowerCamelCase	)
                   # Normalize

                   if self.size_divisibility > 0:
                         raise NotImplementedError()
                   # pad
                   lowerCamelCase__		:    Optional[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_		(		_A      :   str   , _A      :   Optional[int]						):

      """simple docstring"""




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






def 				lowercase_		(		_A      :   Optional[Any]   , _A      :   Tuple[int, int]						):

      """simple docstring"""




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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "AutoFeatureExtractor"
       A__ 						=      "Speech2Text2Tokenizer"





       def __init__(       self							:      List[str] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             super().__init__(__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  self.feature_extractor
             lowerCamelCase__		:    Optional[int]   =  False





       def __call__(       self							:      List[str] ,				*__lowerCamelCase							:      Union[str, Any] ,				**__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             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."	)
                   lowerCamelCase__		:    List[Any]   =  kwargs.pop("raw_speech"	)
             else:
                   lowerCamelCase__		:    str   =  kwargs.pop("audio" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  kwargs.pop("sampling_rate" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  kwargs.pop("text" ,				__lowerCamelCase	)
             if len(__lowerCamelCase	) > 0:
                   lowerCamelCase__		:    Dict   =  args[0]
                   lowerCamelCase__		:    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:
                   lowerCamelCase__		:    str   =  self.feature_extractor(__lowerCamelCase ,				*__lowerCamelCase ,				sampling_rate=__lowerCamelCase ,				**__lowerCamelCase	)
             if text is not None:
                   lowerCamelCase__		:    Optional[int]   =  self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase	)

             if text is None:
                   return inputs
             elif audio is None:
                   return encodings
             else:
                   lowerCamelCase__		:    Optional[int]   =  encodings["input_ids"]
                   return inputs





       def 						lowerCAmelCase					(       self							:      Any ,				*__lowerCamelCase							:      Any ,				**__lowerCamelCase							:      Any	):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict ,				*__lowerCamelCase							:      int ,				**__lowerCamelCase							:      List[str]	):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       @contextmanager
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             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."	)
             lowerCamelCase__		:    int   =  True
             lowerCamelCase__		:    List[Any]   =  self.tokenizer
             yield
             lowerCamelCase__		:    Optional[int]   =  self.feature_extractor
             lowerCamelCase__		:    List[str]   =  False


