
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    ,  __magic_name__    ,  __magic_name__=sys.maxsize      ):
       lowerCamelCase		:	Any							=      """bilinear"""
       lowerCamelCase		:	Optional[int]							=      max_size
       lowerCamelCase		:	Dict							=      short_edge_length






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

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

       return img_augs





class 							A__		:
      def __init__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,  cfg.INPUT.MAX_SIZE_TEST      )
       lowerCamelCase		:	Any							=      cfg.INPUT.FORMAT
       lowerCamelCase		:	str							=      cfg.SIZE_DIVISIBILITY
       lowerCamelCase		:	List[str]							=      cfg.PAD_VALUE
       lowerCamelCase		:	Any							=      cfg.INPUT.MAX_SIZE_TEST
       lowerCamelCase		:	List[Any]							=      cfg.MODEL.DEVICE
       lowerCamelCase		:	Union[str, 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 __magic_name__      : (x - self.pixel_mean) / self.pixel_std
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      tuple(max(__magic_name__      ) for s in zip(*[img.shape for img in images]      )      )
       lowerCamelCase		:	Union[str, Any]							=      [im.shape[-2:] for im in images]
       lowerCamelCase		:	Optional[Any]							=      [
           nn.functional.pad(
               __magic_name__    ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,  value=self.pad_value    ,  )
           for size, im in zip(__magic_name__    ,  __magic_name__      )
       ]

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






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

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





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 assert torch.isfinite(lowerCamelCase							).all(), "Box tensor contains infinite or NaN!"
 lowerCamelCase			,    lowerCamelCase		:	List[str]							=      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							)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




