



from typing import TYPE_CHECKING

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


UpperCamelCase_      			=  {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["LayoutLMTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LayoutLMForMaskedLM",
							    "LayoutLMForSequenceClassification",
							    "LayoutLMForTokenClassification",
							    "LayoutLMForQuestionAnswering",
							    "LayoutLMModel",
							    "LayoutLMPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFLayoutLMForMaskedLM",
							    "TFLayoutLMForSequenceClassification",
							    "TFLayoutLMForTokenClassification",
							    "TFLayoutLMForQuestionAnswering",
							    "TFLayoutLMMainLayer",
							    "TFLayoutLMModel",
							    "TFLayoutLMPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

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








import numpy as np







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





