






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    SCREAMING_SNAKE_CASE		=			'''mobilebert'''






    def __init__( self	,A=30_522	,A=512	,A=24	,A=4	,A=512	,A="relu"	,A=0.0	,A=0.1	,A=512	,A=2	,A=0.02	,A=1e-1_2	,A=0	,A=128	,A=True	,A=True	,A=128	,A=False	,A=True	,A=4	,A="no_norm"	,A=True	,A=None	,**A	,):
          super().__init__(pad_token_id=A	,**A					)

          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       hidden_size
          UpperCAmelCase							=       num_hidden_layers
          UpperCAmelCase							=       num_attention_heads
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       intermediate_size
          UpperCAmelCase							=       hidden_dropout_prob
          UpperCAmelCase							=       attention_probs_dropout_prob
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       type_vocab_size
          UpperCAmelCase							=       initializer_range
          UpperCAmelCase							=       layer_norm_eps
          UpperCAmelCase							=       embedding_size
          UpperCAmelCase							=       trigram_input
          UpperCAmelCase							=       use_bottleneck
          UpperCAmelCase							=       intra_bottleneck_size
          UpperCAmelCase							=       use_bottleneck_attention
          UpperCAmelCase							=       key_query_shared_bottleneck
          UpperCAmelCase							=       num_feedforward_networks
          UpperCAmelCase							=       normalization_type
          UpperCAmelCase							=       classifier_activation

          if self.use_bottleneck:
                UpperCAmelCase							=       intra_bottleneck_size
          else:
                UpperCAmelCase							=       hidden_size

          UpperCAmelCase							=       classifier_dropout







class 					lowerCamelCase__					(       snake_case ):






    @property
    def 				_UpperCamelCase							( self					):
          if self.task == "multiple-choice":
                UpperCAmelCase							=       {0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
                UpperCAmelCase							=       {0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
                  ("""token_type_ids""", dynamic_axis),
              ]					)











"""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



