





"""simple docstring"""



import numpy as np







def 	__lowercase  (     snake_case_						:						Tuple       ,snake_case_						:						str       ,snake_case_						:						List[str]		)     ->List[Any]:



				'''simple docstring'''







				__A					:		List[str]						=  n_draws * class_counts / class_counts.sum()
				# floored means we don't overshoot n_samples, but probably undershoot
				__A					:		Tuple						=  np.floor(snake_case_		)
				# we add samples according to how much "left over" probability
				# they had, until we arrive at n_samples
				__A					:		Tuple						=  int(n_draws - floored.sum()		)
				if need_to_add > 0:
								__A					:		int						=  continuous - floored
								__A					:		Any						=  np.sort(np.unique(snake_case_		)		)[::-1]
								# add according to remainder, but break ties
								# randomly to avoid biases
								for value in values:
												((__A    )				,     )					:		Dict						=  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
												__A					:		Tuple						=  min(len(snake_case_		)       ,snake_case_		)
												__A					:		Dict						=  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 	__lowercase  (     snake_case_						:						Tuple       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						int       ,snake_case_						:						Any       ,snake_case_						:						Optional[int]=10		)     ->List[Any]:



				'''simple docstring'''







				__A				, __A					:		Union[str, Any]						=  np.unique(snake_case_       ,return_inverse=snake_case_		)
				__A					:		List[Any]						=  classes.shape[0]
				__A					:		Union[str, Any]						=  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)		)
				__A					:		int						=  np.split(np.argsort(snake_case_       ,kind='''mergesort'''		)       ,np.cumsum(snake_case_		)[:-1]		)
				for _ in range(snake_case_		):
								__A					:		Tuple						=  approximate_mode(snake_case_       ,snake_case_       ,snake_case_		)
								__A					:		List[Any]						=  class_counts - n_i
								__A					:		List[str]						=  approximate_mode(snake_case_       ,snake_case_       ,snake_case_		)

								__A					:		Optional[Any]						=  []
								__A					:		Optional[Any]						=  []

								for i in range(snake_case_		):
												__A					:		Optional[int]						=  rng.permutation(class_counts[i]		)
												__A					:		Any						=  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]]		)
								__A					:		Dict						=  rng.permutation(snake_case_		)
								__A					:		Dict						=  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_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

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




