code: 
  
  
  
  
  
  '''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_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=sys.maxsize )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    '''bilinear'''
          _SCREAMING_SNAKE_CASE             =    max_size
          _SCREAMING_SNAKE_CASE             =    short_edge_length
  
  
  
  
  
      def __call__(	self				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    []
          for img in imgs:
              _SCREAMING_SNAKE_CASE             =    img.shape[:2]
              # later: provide list and randomly choose index for resize
              _SCREAMING_SNAKE_CASE             =    np.random.randint(self.short_edge_length[0]				,	self.short_edge_length[1] + 1 )
              if size == 0:
                  return img
              _SCREAMING_SNAKE_CASE             =    size * 1.0 / min(__UpperCamelCase				,	__UpperCamelCase )
              if h < w:
                  _SCREAMING_SNAKE_CASE             =    size, scale * w
              else:
                  _SCREAMING_SNAKE_CASE             =    scale * h, size
              if max(__UpperCamelCase				,	__UpperCamelCase ) > self.max_size:
                  _SCREAMING_SNAKE_CASE             =    self.max_size * 1.0 / max(__UpperCamelCase				,	__UpperCamelCase )
                  _SCREAMING_SNAKE_CASE             =    newh * scale
                  _SCREAMING_SNAKE_CASE             =    neww * scale
              _SCREAMING_SNAKE_CASE             =    int(neww + 0.5 )
              _SCREAMING_SNAKE_CASE             =    int(newh + 0.5 )
  
              if img.dtype == np.uinta:
                  _SCREAMING_SNAKE_CASE             =    Image.fromarray(__UpperCamelCase )
                  _SCREAMING_SNAKE_CASE             =    pil_image.resize((neww, newh)				,	PILImageResampling.BILINEAR )
                  _SCREAMING_SNAKE_CASE             =    np.asarray(__UpperCamelCase )
              else:
                  _SCREAMING_SNAKE_CASE             =    img.permute(2				,	0				,	1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
                  _SCREAMING_SNAKE_CASE             =    nn.functional.interpolate(
                      __UpperCamelCase				,	(newh, neww)				,	mode=self.interp_method				,	align_corners=__UpperCamelCase ).squeeze(0 )
              img_augs.append(__UpperCamelCase )
  
          return img_augs
  
  
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,	cfg.INPUT.MAX_SIZE_TEST )
          _SCREAMING_SNAKE_CASE             =    cfg.INPUT.FORMAT
          _SCREAMING_SNAKE_CASE             =    cfg.SIZE_DIVISIBILITY
          _SCREAMING_SNAKE_CASE             =    cfg.PAD_VALUE
          _SCREAMING_SNAKE_CASE             =    cfg.INPUT.MAX_SIZE_TEST
          _SCREAMING_SNAKE_CASE             =    cfg.MODEL.DEVICE
          _SCREAMING_SNAKE_CASE             =    torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )				,	1				,	1 )
          _SCREAMING_SNAKE_CASE             =    torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )				,	1				,	1 )
          _SCREAMING_SNAKE_CASE             =    lambda A : (x - self.pixel_mean) / self.pixel_std
  
  
      def     snake_case_(	self				,	A )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    tuple(max(__UpperCamelCase ) for s in zip(*[img.shape for img in images] ) )
          _SCREAMING_SNAKE_CASE             =    [im.shape[-2:] for im in images]
          _SCREAMING_SNAKE_CASE             =    [
              nn.functional.pad(
                  __UpperCamelCase				,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,	value=self.pad_value				,	)
              for size, im in zip(__UpperCamelCase				,	__UpperCamelCase )
          ]
  
          return torch.stack(__UpperCamelCase ), torch.tensor(__UpperCamelCase )
  
  
  
  
  
      def __call__(	self				,	A				,	A=False )	->     str:
  
  
          with torch.no_grad():
              if not isinstance(__UpperCamelCase				,	__UpperCamelCase ):
                  _SCREAMING_SNAKE_CASE             =    [images]
              if single_image:
                  assert len(__UpperCamelCase ) == 1
              for i in range(len(__UpperCamelCase ) ):
                  if isinstance(images[i]				,	torch.Tensor ):
                      images.insert(__UpperCamelCase				,	images.pop(__UpperCamelCase ).to(self.device ).float() )
                  elif not isinstance(images[i]				,	torch.Tensor ):
                      images.insert(
                          __UpperCamelCase				,	torch.as_tensor(img_tensorize(images.pop(__UpperCamelCase )				,	input_format=self.input_format ) )
                          .to(self.device )
                          .float()				,	)
              # resize smallest edge
              _SCREAMING_SNAKE_CASE             =    torch.tensor([im.shape[:2] for im in images] )
              _SCREAMING_SNAKE_CASE             =    self.aug(__UpperCamelCase )
              # 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
              _SCREAMING_SNAKE_CASE             =    [self.normalizer(__UpperCamelCase ) for x in images]
              # now pad them to do the following operations
              _SCREAMING_SNAKE_CASE             =    self.pad(__UpperCamelCase )
              # Normalize
  
              if self.size_divisibility > 0:
                  raise NotImplementedError()
              # pad
              _SCREAMING_SNAKE_CASE             =    torch.true_divide(__UpperCamelCase				,	__UpperCamelCase )
              if single_image:
                  return images[0], sizes[0], scales_yx[0]
              else:
                  return images, sizes, scales_yx
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					List[Any]					)		->List[Any]:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Dict      , __lowerCamelCase				:					int					)		->int:
      assert torch.isfinite(snake_case_					).all(), "Box tensor contains infinite or NaN!"
      _SCREAMING_SNAKE_CASE             =    box_size
      tensor[:, 0].clamp_(min=0      , max=snake_case_					)
      tensor[:, 1].clamp_(min=0      , max=snake_case_					)
      tensor[:, 2].clamp_(min=0      , max=snake_case_					)
      tensor[:, 3].clamp_(min=0      , max=snake_case_					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      if name != "training_function" and name != "main":
          raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_SNAKE_CASE             =    f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  