






"""simple docstring"""





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_SCREAMING_SNAKE_CASE :    Optional[Any]    =						importlib.metadata.version('''numpy''')
_SCREAMING_SNAKE_CASE :    Any    =						'''.'''.join([str(x) for x in sys.version_info[:3]])






class 					a						( __snake_case		):






    def        UpperCamelCase	(	self					:							List[str] )					->		List[str]:
      # lt + different version strings
      require_version_core('numpy<1000.4.5' )
      require_version_core('numpy<1000.4' )
      require_version_core('numpy<1000' )

      # le
      require_version_core('numpy<=1000.4.5' )
      require_version_core(F'''numpy<={numpy_ver}''' )

      # eq
      require_version_core(F'''numpy=={numpy_ver}''' )

      # ne
      require_version_core('numpy!=1000.4.5' )

      # ge
      require_version_core('numpy>=1.0' )
      require_version_core('numpy>=1.0.0' )
      require_version_core(F'''numpy>={numpy_ver}''' )

      # gt
      require_version_core('numpy>1.0.0' )

      # mix
      require_version_core('numpy>1.0.0,<1000' )

      # requirement w/o version
      require_version_core('numpy' )

      # unmet requirements due to version conflict
      for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
        try:
          require_version_core(__SCREAMING_SNAKE_CASE )
        except ImportError as e:
          self.assertIn(F'''{req} is required'''	,						str(__SCREAMING_SNAKE_CASE ) )
          self.assertIn('but found'	,						str(__SCREAMING_SNAKE_CASE ) )

        # unmet requirements due to missing module
      for req in ["numpipypie>1", "numpipypie2"]:
        try:
          require_version_core(__SCREAMING_SNAKE_CASE )
        except importlib.metadata.PackageNotFoundError as e:
          self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''	,						str(__SCREAMING_SNAKE_CASE ) )
          self.assertIn('Try: pip install transformers -U'	,						str(__SCREAMING_SNAKE_CASE ) )

        # bogus requirements formats:
        # 1. whole thing
      for req in ["numpy??1.0.0", "numpy1.0.0"]:
        try:
          require_version_core(__SCREAMING_SNAKE_CASE )
        except ValueError as e:
          self.assertIn('requirement needs to be in the pip package format'	,						str(__SCREAMING_SNAKE_CASE ) )
        # 2. only operators
      for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
        try:
          require_version_core(__SCREAMING_SNAKE_CASE )
        except ValueError as e:
          self.assertIn('need one of '	,						str(__SCREAMING_SNAKE_CASE ) )






    def        UpperCamelCase	(	self					:							Dict )					->		str:
      # matching requirement
      require_version('python>=3.6.0' )

      # not matching requirements
      for req in ["python>9.9.9", "python<3.0.0"]:
        try:
          require_version_core(__SCREAMING_SNAKE_CASE )
        except ImportError as e:
          self.assertIn(F'''{req} is required'''	,						str(__SCREAMING_SNAKE_CASE ) )
          self.assertIn(F'''but found python=={python_ver}'''	,						str(__SCREAMING_SNAKE_CASE ) )












"""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




