


import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



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






def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					List[Any]=10  ):







 """simple docstring"""



 UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= np.unique(SCREAMING_SNAKE_CASE     ,			return_inverse=SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     str 							= classes.shape[0]
 UpperCamelCase__       :     List[Any] 							= np.bincount(SCREAMING_SNAKE_CASE  )
 if np.min(SCREAMING_SNAKE_CASE  ) < 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__       :     Optional[int] 							= np.split(np.argsort(SCREAMING_SNAKE_CASE     ,			kind='''mergesort'''  )     ,			np.cumsum(SCREAMING_SNAKE_CASE  )[:-1]  )
 for _ in range(SCREAMING_SNAKE_CASE  ):
  UpperCamelCase__       :     Tuple 							= approximate_mode(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
  UpperCamelCase__       :     List[str] 							= class_counts - n_i
  UpperCamelCase__       :     Union[str, Any] 							= approximate_mode(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

  UpperCamelCase__       :     Optional[int] 							= []
  UpperCamelCase__       :     Dict 							= []

  for i in range(SCREAMING_SNAKE_CASE  ):
   UpperCamelCase__       :     Optional[int] 							= rng.permutation(class_counts[i]  )
   UpperCamelCase__       :     int 							= class_indices[i].take(SCREAMING_SNAKE_CASE     ,			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__       :     List[Any] 							= rng.permutation(SCREAMING_SNAKE_CASE  )
  UpperCamelCase__       :     Any 							= rng.permutation(SCREAMING_SNAKE_CASE  )

  yield train, test






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							Dict    							=					{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Any    							=					[
           "DPRContextEncoderTokenizerFast",
           "DPRQuestionEncoderTokenizerFast",
           "DPRReaderTokenizerFast",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							str    							=					[
           "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPRContextEncoder",
           "DPRPretrainedContextEncoder",
           "DPRPreTrainedModel",
           "DPRPretrainedQuestionEncoder",
           "DPRPretrainedReader",
           "DPRQuestionEncoder",
           "DPRReader",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							int    							=					[
           "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDPRContextEncoder",
           "TFDPRPretrainedContextEncoder",
           "TFDPRPretrainedQuestionEncoder",
           "TFDPRPretrainedReader",
           "TFDPRQuestionEncoder",
           "TFDPRReader",
       ]


if TYPE_CHECKING:
       from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
       from .tokenization_dpr import (
           DPRContextEncoderTokenizer,
           DPRQuestionEncoderTokenizer,
           DPRReaderOutput,
           DPRReaderTokenizer,
       )

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_dpr_fast import (
                  DPRContextEncoderTokenizerFast,
                  DPRQuestionEncoderTokenizerFast,
                  DPRReaderTokenizerFast,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dpr import (
                  DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPRContextEncoder,
                  DPRPretrainedContextEncoder,
                  DPRPreTrainedModel,
                  DPRPretrainedQuestionEncoder,
                  DPRPretrainedReader,
                  DPRQuestionEncoder,
                  DPRReader,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_dpr import (
                  TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDPRContextEncoder,
                  TFDPRPretrainedContextEncoder,
                  TFDPRPretrainedQuestionEncoder,
                  TFDPRPretrainedReader,
                  TFDPRQuestionEncoder,
                  TFDPRReader,
              )

else:
       import sys

       __UpperCamelCase							:							Dict    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


