"""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 					lowerCAmelCase_      :



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=sys.maxsize      ):






          """simple docstring"""

          snake_case        =     'bilinear'
          snake_case        =     max_size
          snake_case        =     short_edge_length

    def __call__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

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

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

          return img_augs





class 					lowerCAmelCase_      :



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,      cfg.INPUT.MAX_SIZE_TEST      )
          snake_case        =     cfg.INPUT.FORMAT
          snake_case        =     cfg.SIZE_DIVISIBILITY
          snake_case        =     cfg.PAD_VALUE
          snake_case        =     cfg.INPUT.MAX_SIZE_TEST
          snake_case        =     cfg.MODEL.DEVICE
          snake_case        =     torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )						,      1						,      1      )
          snake_case        =     torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )						,      1						,      1      )
          snake_case        =     lambda lowerCAmelCase      : (x - self.pixel_mean) / self.pixel_std

    def 						snake_case	(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     tuple(max(lowerCAmelCase      ) for s in zip(*[img.shape for img in images]      )      )
          snake_case        =     [im.shape[-2:] for im in images]
          snake_case        =     [
              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						,      lowerCAmelCase						,      lowerCAmelCase=False      ):






          """simple docstring"""

          with torch.no_grad():
                if not isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
                      snake_case        =     [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
                snake_case        =     torch.tensor([im.shape[:2] for im in images]      )
                snake_case        =     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
                snake_case        =     [self.normalizer(lowerCAmelCase      ) for x in images]
                # now pad them to do the following operations
                snake_case   ,snake_case        =     self.pad(lowerCAmelCase      )
                # Normalize

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[str]   )   ->			Optional[int]:





      """simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Tuple[int, int]   )   ->			Dict:





      """simple docstring"""






      assert torch.isfinite(_UpperCamelCase   ).all(), "Box tensor contains infinite or NaN!"
      snake_case   ,snake_case        =     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 os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


