

import numpy as np
def       UpperCamelCase					(			_lowerCAmelCase     :						List[str] )				->			int:
	_UpperCAmelCase    :							Any									=					np.exp(lowercase__ )

	# Add up the all the exponentials
	_UpperCAmelCase    :							int									=					np.sum(lowercase__ )

	# Divide every exponent by the sum of all exponents
	_UpperCAmelCase    :							Optional[int]									=					exponent_vector / sum_of_exponents

	return softmax_vector


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








"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__				:  Optional[int]													=  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  ['''T5Tokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[str]													=  ['''T5TokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Optional[Any]													=  [
						    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''T5EncoderModel''',
						    '''T5ForConditionalGeneration''',
						    '''T5Model''',
						    '''T5PreTrainedModel''',
						    '''load_tf_weights_in_t5''',
						    '''T5ForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  int													=  [
						    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFT5EncoderModel''',
						    '''TFT5ForConditionalGeneration''',
						    '''TFT5Model''',
						    '''TFT5PreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''FlaxT5EncoderModel''',
						    '''FlaxT5ForConditionalGeneration''',
						    '''FlaxT5Model''',
						    '''FlaxT5PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__				:  Dict													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





