


from typing import TYPE_CHECKING

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


__UpperCAmelCase : Dict 			=				{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : List[Any] 			=				["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : int 			=				["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : str 			=				[
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : Optional[int] 			=				[
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase : List[str] 			=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import requests
from bsa import BeautifulSoup


def      a	(							SCREAMING_SNAKE_CASE_      :		str = "AAPL"       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
       UpperCamelCase    :	Any   	=		BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_       ).text							,			'''html.parser'''       )
       UpperCamelCase    :	Dict   	=		'''My(6px) Pos(r) smartphone_Mt(6px)'''
       return soup.find('''div'''							,			class_=class_       ).find('''span'''       ).text


if __name__ == "__main__":
  for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
    print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')

