


"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase = 1_00       ,    ):







    """simple docstring"""
    a 		=x_start
    a 		=fnc(lowercase		)
    a 		=0.0

    for _ in range(lowercase		):
        # Approximates curve as a sequence of linear lines and sums their length
        a 		=(x_end - x_start) / steps + xa
        a 		=fnc(lowercase		)
        length += math.hypot(xa - xa       ,    fxa - fxa		)

        # Increment step
        a 		=xa
        a 		=fxa

    return length


if __name__ == "__main__":
       def _A						(						lowercase		):







           """simple docstring"""
           return math.sin(10 * x		)

       print("""f(x) = sin(10 * x)""")
       print("""The length of the curve from x = -10 to x = 10 is:""")
       lowerCamelCase_   :   Tuple									=  1_0
       while i <= 1_0_0_0_0_0:
              print(F'With {i} steps: {line_length(f, -1_0, 1_0, i)}')
              i *= 1_0




"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


