





'''simple docstring'''




import numpy as np





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Any     , __snake_case							:    Dict     )							->		Tuple:



       """simple docstring"""
       lowerCamelCase_							=n_draws * class_counts / class_counts.sum()
       # floored means we don't overshoot n_samples, but probably undershoot
       lowerCamelCase_							=np.floor(__snake_case     )
       # 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(__snake_case     )     )[::-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(__snake_case     )     , __snake_case     )
                     lowerCamelCase_							=rng.choice(__snake_case     , size=__snake_case     , replace=__snake_case     )
                     floored[inds] += 1
                     need_to_add -= add_now
                     if need_to_add == 0:
                            break
       return floored.astype(np.intaa     )





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    List[Any]     , __snake_case							:    Any     , __snake_case							:    Tuple     , __snake_case							:    int=10     )							->		Dict:



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

              lowerCamelCase_							=[]
              lowerCamelCase_							=[]

              for i in range(__snake_case     ):
                     lowerCamelCase_							=rng.permutation(class_counts[i]     )
                     lowerCamelCase_							=class_indices[i].take(__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]]     )
              lowerCamelCase_							=rng.permutation(__snake_case     )
              lowerCamelCase_							=rng.permutation(__snake_case     )

              yield train, test










'''simple docstring'''




from typing import TYPE_CHECKING

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


a_   :	Union[str, Any]							   = {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Optional[int]							   = ["""OpenAIGPTTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	int							   = [
         """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OpenAIGPTDoubleHeadsModel""",
         """OpenAIGPTForSequenceClassification""",
         """OpenAIGPTLMHeadModel""",
         """OpenAIGPTModel""",
         """OpenAIGPTPreTrainedModel""",
         """load_tf_weights_in_openai_gpt""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Dict							   = [
         """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFOpenAIGPTDoubleHeadsModel""",
         """TFOpenAIGPTForSequenceClassification""",
         """TFOpenAIGPTLMHeadModel""",
         """TFOpenAIGPTMainLayer""",
         """TFOpenAIGPTModel""",
         """TFOpenAIGPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_openai_fast import OpenAIGPTTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_openai import (
              OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OpenAIGPTDoubleHeadsModel,
              OpenAIGPTForSequenceClassification,
              OpenAIGPTLMHeadModel,
              OpenAIGPTModel,
              OpenAIGPTPreTrainedModel,
              load_tf_weights_in_openai_gpt,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_openai import (
              TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFOpenAIGPTDoubleHeadsModel,
              TFOpenAIGPTForSequenceClassification,
              TFOpenAIGPTLMHeadModel,
              TFOpenAIGPTMainLayer,
              TFOpenAIGPTModel,
              TFOpenAIGPTPreTrainedModel,
          )

else:
     import sys

     a_   :	List[Any]							   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




