import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     _SCREAMING_SNAKE_CASE   		=			"platform"

     import jax
     import jax.numpy as jnp

     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=None       ,				UpperCamelCase_=None       ,				UpperCamelCase_=None       ,				UpperCamelCase_=None       ,				UpperCamelCase_=None       ,				UpperCamelCase_=None       ,				)		->  str:







 '''simple docstring'''






 if attention_mask is None:
  UpperCamelCase      =			np.where(input_ids != config.pad_token_id       ,				1       ,				0					)
 if decoder_attention_mask is None:
  UpperCamelCase      =			np.where(decoder_input_ids != config.pad_token_id       ,				1       ,				0					)
 if head_mask is None:
  UpperCamelCase      =			np.ones((config.encoder_layers, config.encoder_attention_heads)					)
 if decoder_head_mask is None:
  UpperCamelCase      =			np.ones((config.decoder_layers, config.decoder_attention_heads)					)
 if cross_attn_head_mask is None:
  UpperCamelCase      =			np.ones((config.decoder_layers, config.decoder_attention_heads)					)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }
class SCREAMING_SNAKE_CASE_		:

   '''simple docstring'''





   def __init__(    self	:	Optional[int]      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	Optional[Any]=13      ,      lowerCamelCase_	:	Optional[Any]=7      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	Tuple=False      ,      lowerCamelCase_	:	Union[str, Any]=99      ,      lowerCamelCase_	:	Tuple=16      ,      lowerCamelCase_	:	Optional[int]=2      ,      lowerCamelCase_	:	List[Any]=4      ,      lowerCamelCase_	:	int=4      ,      lowerCamelCase_	:	Any="gelu"      ,      lowerCamelCase_	:	List[Any]=0.1      ,      lowerCamelCase_	:	Dict=0.1      ,      lowerCamelCase_	:	str=32      ,      lowerCamelCase_	:	str=2      ,      lowerCamelCase_	:	Union[str, Any]=1      ,      lowerCamelCase_	:	List[Any]=0      ,      lowerCamelCase_	:	Union[str, Any]=0.0_2      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			seq_length
    UpperCamelCase      =			is_training
    UpperCamelCase      =			use_labels
    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			eos_token_id
    UpperCamelCase      =			pad_token_id
    UpperCamelCase      =			bos_token_id
    UpperCamelCase      =			initializer_range







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





    """simple docstring"""


    UpperCamelCase      =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,      self.vocab_size							)      ,      3      ,      self.vocab_size							)
    UpperCamelCase      =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,      dtype=np.intaa							))      ,      -1							)

    UpperCamelCase      =			shift_tokens_right(__snake_case      ,      1      ,      2							)

    UpperCamelCase      =			MarianConfig(
        vocab_size=self.vocab_size      ,      d_model=self.hidden_size      ,      encoder_layers=self.num_hidden_layers      ,      decoder_layers=self.num_hidden_layers      ,      encoder_attention_heads=self.num_attention_heads      ,      decoder_attention_heads=self.num_attention_heads      ,      encoder_ffn_dim=self.intermediate_size      ,      decoder_ffn_dim=self.intermediate_size      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      eos_token_id=self.eos_token_id      ,      bos_token_id=self.bos_token_id      ,      pad_token_id=self.pad_token_id      ,      initializer_range=self.initializer_range      ,      use_cache=__snake_case      ,      )
    UpperCamelCase      =			prepare_marian_inputs_dict(__snake_case      ,      __snake_case      ,      __snake_case							)
    return config, inputs_dict







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.prepare_config_and_inputs()
    return config, inputs_dict







   def 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			20
    UpperCamelCase      =			model_class_name(__snake_case							)

    UpperCamelCase      =			model.encode(inputs_dict["""input_ids"""]							)

    UpperCamelCase     ,    UpperCamelCase      =			(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    UpperCamelCase      =			model.init_cache(decoder_input_ids.shape[0]      ,      __snake_case      ,      __snake_case							)
    UpperCamelCase      =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,      dtype="""i4"""							)

    UpperCamelCase      =			jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )
    UpperCamelCase      =			model.decode(
        decoder_input_ids[:, :-1]      ,      __snake_case      ,      decoder_attention_mask=__snake_case      ,      past_key_values=__snake_case      ,      decoder_position_ids=__snake_case      ,      )

    UpperCamelCase      =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,      dtype="""i4"""							)
    UpperCamelCase      =			model.decode(
        decoder_input_ids[:, -1:]      ,      __snake_case      ,      decoder_attention_mask=__snake_case      ,      past_key_values=outputs_cache.past_key_values      ,      decoder_position_ids=__snake_case      ,      )

    UpperCamelCase      =			model.decode(__snake_case      ,      __snake_case							)

    UpperCamelCase      =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
    self.parent.assertTrue(diff < 1E-3      ,      msg=f"""Max diff is {diff}"""							)







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





    """simple docstring"""


    UpperCamelCase      =			20
    UpperCamelCase      =			model_class_name(__snake_case							)

    UpperCamelCase      =			model.encode(inputs_dict["""input_ids"""]							)

    UpperCamelCase     ,    UpperCamelCase      =			(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    UpperCamelCase      =			jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
        ]      ,      axis=-1      ,      )

    UpperCamelCase      =			model.init_cache(decoder_input_ids.shape[0]      ,      __snake_case      ,      __snake_case							)
    UpperCamelCase      =			jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )

    UpperCamelCase      =			model.decode(
        decoder_input_ids[:, :-1]      ,      __snake_case      ,      decoder_attention_mask=__snake_case      ,      past_key_values=__snake_case      ,      decoder_position_ids=__snake_case      ,      )
    UpperCamelCase      =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,      dtype="""i4"""							)
    UpperCamelCase      =			model.decode(
        decoder_input_ids[:, -1:]      ,      __snake_case      ,      past_key_values=outputs_cache.past_key_values      ,      decoder_attention_mask=__snake_case      ,      decoder_position_ids=__snake_case      ,      )

    UpperCamelCase      =			model.decode(__snake_case      ,      __snake_case      ,      decoder_attention_mask=__snake_case							)

    UpperCamelCase      =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
    self.parent.assertTrue(diff < 1E-3      ,      msg=f"""Max diff is {diff}"""							)





@require_flax
class SCREAMING_SNAKE_CASE_		(			snake_case__     ,    unittest.TestCase     ,    snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							True
   __lowerCAmelCase											=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   __lowerCAmelCase											=							(FlaxMarianMTModel,) if is_flax_available() else ()







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			FlaxMarianModelTester(self							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(__snake_case      ,      __snake_case      ,      __snake_case							)







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





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case      ,      __snake_case      ,      __snake_case							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__							):
      UpperCamelCase      =			self._prepare_for_class(__snake_case      ,      __snake_case							)
      UpperCamelCase      =			model_class(__snake_case							)

      @jax.jit
      def encode_jitted(lowerCamelCase_	:	Tuple      ,      lowerCamelCase_	:	Optional[int]=None      ,      **lowerCamelCase_	:	List[Any]							):
       return model.encode(input_ids=__snake_case      ,      attention_mask=__snake_case							)

      with self.subTest("""JIT Enabled"""							):
       UpperCamelCase      =			encode_jitted(**__snake_case							).to_tuple()

      with self.subTest("""JIT Disabled"""							):
       with jax.disable_jit():
        UpperCamelCase      =			encode_jitted(**__snake_case							).to_tuple()

      self.assertEqual(len(__snake_case							)      ,      len(__snake_case							)							)
      for jitted_output, output in zip(__snake_case      ,      __snake_case							):
       self.assertEqual(jitted_output.shape      ,      output.shape							)







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__							):
      UpperCamelCase      =			model_class(__snake_case							)
      UpperCamelCase      =			model.encode(inputs_dict["""input_ids"""]      ,      inputs_dict["""attention_mask"""]							)

      UpperCamelCase      =			{
          """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
          """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
          """encoder_outputs""": encoder_outputs,
      }

      @jax.jit
      def decode_jitted(lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[int]							):
       return model.decode(
           decoder_input_ids=__snake_case      ,      decoder_attention_mask=__snake_case      ,      encoder_outputs=__snake_case      ,      )

      with self.subTest("""JIT Enabled"""							):
       UpperCamelCase      =			decode_jitted(**__snake_case							).to_tuple()

      with self.subTest("""JIT Disabled"""							):
       with jax.disable_jit():
        UpperCamelCase      =			decode_jitted(**__snake_case							).to_tuple()

      self.assertEqual(len(__snake_case							)      ,      len(__snake_case							)							)
      for jitted_output, output in zip(__snake_case      ,      __snake_case							):
       self.assertEqual(jitted_output.shape      ,      output.shape							)







   @slow
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    for model_class_name in self.all_model_classes:
     UpperCamelCase      =			model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""							)
     # FlaxMarianForSequenceClassification expects eos token in input_ids
     UpperCamelCase      =			np.ones((1, 1)							) * model.config.eos_token_id
     UpperCamelCase      =			model(__snake_case							)
     self.assertIsNotNone(__snake_case							)





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):

   '''simple docstring'''





   __lowerCAmelCase											=							None
   __lowerCAmelCase											=							None







   @classmethod
   def 	lowerCamelCase_  (    cls	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
    return cls







   @cached_property
   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    return MarianTokenizer.from_pretrained(self.model_name							)







   @property
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return self.tokenizer.eos_token_id







   @cached_property
   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			FlaxMarianMTModel.from_pretrained(self.model_name							)
    self.assertEqual(model.config.decoder_start_token_id      ,      model.config.pad_token_id							)
    return model







   def 	lowerCamelCase_  (    self	:	str      ,      **lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.translate_src_text(**__snake_case							)
    self.assertListEqual(self.expected_text      ,      __snake_case							)







   def 	lowerCamelCase_  (    self	:	str      ,      **lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase      =			self.tokenizer(self.src_text      ,      padding=__snake_case      ,      return_tensors="""np"""      ,      **__snake_case							)
    UpperCamelCase      =			self.model.generate(
        model_inputs.input_ids      ,      attention_mask=model_inputs.attention_mask      ,      num_beams=2      ,      max_length=128      ,      ).sequences
    UpperCamelCase      =			self.tokenizer.batch_decode(__snake_case      ,      skip_special_tokens=__snake_case							)
    return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							"""fr"""
   __lowerCAmelCase											=							[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
   ]
   __lowerCAmelCase											=							[
       """Je suis une petite grenouille.""",
       """Maintenant, je peux oublier les 100 mots d\'allemand que je connais.""",
   ]







   @slow
   def 	lowerCamelCase_  (    self	:	List[Any]							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""fr"""
   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							[
       """Donnez moi le micro.""",
       """Tom et Mary étaient assis à une table.""",  # Accents
   ]
   __lowerCAmelCase											=							[
       """Give me the microphone.""",
       """Tom and Mary were sitting at a table.""",
   ]







   @slow
   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""mt"""
   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							["""Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
   __lowerCAmelCase											=							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]







   @slow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							"""de"""
   __lowerCAmelCase											=							[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
       """Tom asked his teacher for advice.""",
       """That\'s how I would do it.""",
       """Tom really admired Mary\'s courage.""",
       """Turn around and close your eyes.""",
   ]
   __lowerCAmelCase											=							[
       """Ich bin ein kleiner Frosch.""",
       """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
       """Tom bat seinen Lehrer um Rat.""",
       """So würde ich das machen.""",
       """Tom bewunderte Marias Mut wirklich.""",
       """Drehen Sie sich um und schließen Sie die Augen.""",
   ]







   @slow
   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							"""zh"""
   __lowerCAmelCase											=							["""My name is Wolfgang and I live in Berlin"""]
   __lowerCAmelCase											=							["""我叫沃尔夫冈 我住在柏林"""]







   @slow
   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""ru"""
   __lowerCAmelCase											=							"""fr"""
   __lowerCAmelCase											=							["""Он показал мне рукопись своей новой пьесы."""]
   __lowerCAmelCase											=							["""Il m\'a montré le manuscrit de sa nouvelle pièce."""]







   @slow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			snake_case__		):

   '''simple docstring'''





   __lowerCAmelCase											=							"""en"""
   __lowerCAmelCase											=							"""ROMANCE"""
   __lowerCAmelCase											=							[
       """>>fr<< Don\'t spend so much time watching TV.""",
       """>>pt<< Your message has been sent.""",
       """>>es<< He\'s two years older than me.""",
   ]
   __lowerCAmelCase											=							[
       """Ne passez pas autant de temps à regarder la télé.""",
       """A sua mensagem foi enviada.""",
       """Es dos años más viejo que yo.""",
   ]







   @slow
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()

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
