




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_      						=   {
    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
    "tokenization_flaubert": ["FlaubertTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_      						=   [
     "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaubertForMultipleChoice",
     "FlaubertForQuestionAnswering",
     "FlaubertForQuestionAnsweringSimple",
     "FlaubertForSequenceClassification",
     "FlaubertForTokenClassification",
     "FlaubertModel",
     "FlaubertWithLMHeadModel",
     "FlaubertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_      						=   [
     "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFFlaubertForMultipleChoice",
     "TFFlaubertForQuestionAnsweringSimple",
     "TFFlaubertForSequenceClassification",
     "TFFlaubertForTokenClassification",
     "TFFlaubertModel",
     "TFFlaubertPreTrainedModel",
     "TFFlaubertWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

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














"""simple docstring"""







import sys

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      if not numbers:
            return 0
      if target == 0 and target in numbers:
            return 0
      if not isinstance(_snake_case      ,       (list, tuple)							) or not all(
          isinstance(_snake_case      ,       _snake_case							) for number in numbers							):
            raise ValueError("""numbers must be an iterable of integers"""							)

      UpperCAmelCase							=       UpperCAmelCase							=       UpperCAmelCase							=       0
      UpperCAmelCase							=       sys.maxsize

      while right < len(_snake_case							):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                  UpperCAmelCase							=       min(_snake_case      ,       right - left + 1							)
                  curr_sum -= numbers[left]
                  left += 1
            right += 1

      return 0 if min_len == sys.maxsize else min_len



