






"""simple docstring"""





import os
from typing import List







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		str			)				->    List[Any]:
  if name != "training_function" and name != "main":
    raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''			)
  lowerCamelCase_   					=						[], False
  for line in lines:
    if not found_start and F'''def {name}''' in line:
      lowerCamelCase_   					=						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]			)				->    Optional[int]:
  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			)				->    Optional[int]:
  with open(_lowerCAmelCase		,    'r'			) as f:
    lowerCamelCase_   					=						f.readlines()
  with open(os.path.abspath(os.path.join('examples'		,    'nlp_example.py'			)			)		,    'r'			) as f:
    lowerCamelCase_   					=						f.readlines()
  with open(_lowerCAmelCase		,    'r'			) as f:
    lowerCamelCase_   					=						f.readlines()
  if secondary_filename is not None:
    with open(_lowerCAmelCase		,    'r'			) as f:
      lowerCamelCase_   					=						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:
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'main'			)			)
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'main'			)			)
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'main'			)			)
    if secondary_filename is not None:
      lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'main'			)			)
  else:
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'training_function'			)			)
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'training_function'			)			)
    lowerCamelCase_   					=						clean_lines(get_function_contents_by_name(_lowerCAmelCase		,    'training_function'			)			)
    if secondary_filename is not None:
      lowerCamelCase_   					=						clean_lines(
          get_function_contents_by_name(_lowerCAmelCase		,    'training_function'			)			)

  lowerCamelCase_   					=						'''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
  lowerCamelCase_   					=						[]
  lowerCamelCase_   					=						[]  # We keep track of the idxs just in case it's a repeated statement
  lowerCamelCase_   					=						iter(_lowerCAmelCase			)
  for i in range(len(_lowerCAmelCase			) - 1			):
    if i not in passed_idxs:
      lowerCamelCase_   					=						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
          lowerCamelCase_   					=						next(_lowerCAmelCase			)

    # Extract out just the new parts from the full_file_training_func
  lowerCamelCase_   					=						[]
  lowerCamelCase_   					=						[]  # 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
  lowerCamelCase_   					=						[line for line in new_feature_code if line not in new_full_example_parts]
  if secondary_filename is not None:
    lowerCamelCase_   					=						[line for line in full_file_contents if line not in secondary_file_func]
    lowerCamelCase_   					=						[line for line in diff_from_example if line not in diff_from_two]

  return diff_from_example












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




