



from __future__ import annotations

import math
from collections.abc import Callable



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Callable[[int | float], int | float] ,       UpperCamelCase_				:       int | float ,       UpperCamelCase_				:       int | float ,       UpperCamelCase_				:       int = 1_0_0 ,       )->  float:
    A__		 =					x_start
    A__		 =					fnc(UpperCamelCase_	)
    A__		 =					0.0

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

        # Increment step
        A__		 =					xa
        A__		 =					fxa

    return length


if __name__ == "__main__":



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Union[str, Any]:
       return math.sin(1_0 * x	)

   print("f(x) = sin(10 * x)")
   print("The length of the curve from x = -10 to x = 10 is:")
   _lowercase						  =		10
   while i <= 100000:
      print(F"With {i} steps: {line_length(f, -10, 10, i)}")
      i *= 10





# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase						  =		{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CpmAntForCausalLM",
       "CpmAntModel",
       "CpmAntPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

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