import importlib.metadata
import sys

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


_SCREAMING_SNAKE_CASE   		=			importlib.metadata.version("""numpy""")
_SCREAMING_SNAKE_CASE   		=			""".""".join([str(x) for x in sys.version_info[:3]])
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):

   '''simple docstring'''





   def 	lowerCamelCase_  (    self	:	List[Any]							):





    """simple docstring"""


    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(lowerCamelCase_							)
     except ImportError as e:
      self.assertIn(f"""{req} is required"""      ,      str(lowerCamelCase_							)							)
      self.assertIn("""but found"""      ,      str(lowerCamelCase_							)							)

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

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







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    require_version("""python>=3.6.0"""							)

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

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			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
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 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)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				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]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
