





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 							_UpperCamelCase					:

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: List[Any]=sys.maxsize		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =       'bilinear'
              UpperCamelCase_          =       max_size
              UpperCamelCase_          =       short_edge_length



       def __call__(		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =       []
              for img in imgs:
                     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(__a					,   __a		)
                     if h < w:
                            UpperCamelCase_          =       size, scale * w
                     else:
                            UpperCamelCase_          =       scale * h, size
                     if max(__a					,   __a		) > self.max_size:
                            UpperCamelCase_          =       self.max_size * 1.0 / max(__a					,   __a		)
                            UpperCamelCase_          =       newh * scale
                            UpperCamelCase_          =       neww * scale
                     UpperCamelCase_          =       int(neww + 0.5		)
                     UpperCamelCase_          =       int(newh + 0.5		)

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

              return img_augs



class 							_UpperCamelCase					:

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: Any		)       -> int:




              """simple docstring"""


              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 _SCREAMING_SNAKE_CASE		: (x - self.pixel_mean) / self.pixel_std

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> Dict:




              """simple docstring"""


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

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



       def __call__(		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple=False		)       -> Union[str, Any]:




              """simple docstring"""


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

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Union[str, Any]:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[int]:
       assert torch.isfinite(UpperCamelCase_  ).all(), "Box tensor contains infinite or NaN!"
       UpperCamelCase_          =       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_  )








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   XLMTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

       def lowercase (		self: Dict		)       -> Union[str, Any]:




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file					,   "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file					,   "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: int		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer(self.vocab_file					,   self.merges_file		)

              UpperCamelCase_          =  "lower"
              UpperCamelCase_          =  ["low", "er</w>"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + ["<unk>"]
              UpperCamelCase_          =  [14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer.from_pretrained("xlm-mlm-en-2048"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]

