






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a    :   int        =			{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Optional[int]        =			["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Union[str, Any]        =			[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Any        =			[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 _a    :   Dict        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from __future__ import annotations

import math
from collections.abc import Callable


def 				snake_case__							(							UpperCAmelCase							:  Callable[[int | float], int | float]    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int = 1_0_0    ,			):
    lowerCAmelCase__   :int      							=				x_start
    lowerCAmelCase__   :int      							=				fnc(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				0.0

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

        # Increment step
        lowerCAmelCase__   :Union[str, Any]      							=				xa
        lowerCAmelCase__   :str      							=				fxa

    return length


if __name__ == "__main__":


 def 				snake_case__							(							UpperCAmelCase							:  int       ):
     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:""")
 _a    :   Any        =			10
 while i <= 10_0000:
  print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
  i *= 10



