






from typing import TYPE_CHECKING

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


lowerCamelCase			:					Any         =	{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					Optional[int]         =	['''BertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					Union[str, Any]         =	[
     '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''BertForMaskedLM''',
     '''BertForMultipleChoice''',
     '''BertForNextSentencePrediction''',
     '''BertForPreTraining''',
     '''BertForQuestionAnswering''',
     '''BertForSequenceClassification''',
     '''BertForTokenClassification''',
     '''BertLayer''',
     '''BertLMHeadModel''',
     '''BertModel''',
     '''BertPreTrainedModel''',
     '''load_tf_weights_in_bert''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					Optional[int]         =	[
     '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFBertEmbeddings''',
     '''TFBertForMaskedLM''',
     '''TFBertForMultipleChoice''',
     '''TFBertForNextSentencePrediction''',
     '''TFBertForPreTraining''',
     '''TFBertForQuestionAnswering''',
     '''TFBertForSequenceClassification''',
     '''TFBertForTokenClassification''',
     '''TFBertLMHeadModel''',
     '''TFBertMainLayer''',
     '''TFBertModel''',
     '''TFBertPreTrainedModel''',
 ]
try:
 if not is_tensorflow_text_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					str         =	['''TFBertTokenizer''']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase			:					str         =	[
     '''FlaxBertForCausalLM''',
     '''FlaxBertForMaskedLM''',
     '''FlaxBertForMultipleChoice''',
     '''FlaxBertForNextSentencePrediction''',
     '''FlaxBertForPreTraining''',
     '''FlaxBertForQuestionAnswering''',
     '''FlaxBertForSequenceClassification''',
     '''FlaxBertForTokenClassification''',
     '''FlaxBertModel''',
     '''FlaxBertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
 from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_fast import BertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert import (
      BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertForMaskedLM,
      BertForMultipleChoice,
      BertForNextSentencePrediction,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertForTokenClassification,
      BertLayer,
      BertLMHeadModel,
      BertModel,
      BertPreTrainedModel,
      load_tf_weights_in_bert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bert import (
      TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFBertEmbeddings,
      TFBertForMaskedLM,
      TFBertForMultipleChoice,
      TFBertForNextSentencePrediction,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertForTokenClassification,
      TFBertLMHeadModel,
      TFBertMainLayer,
      TFBertModel,
      TFBertPreTrainedModel,
  )

 try:
  if not is_tensorflow_text_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_tf import TFBertTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bert import (
      FlaxBertForCausalLM,
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
      FlaxBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase			:					Union[str, Any]         =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase			:					int         =	1.0_5457_1817E-34  # unit of ℏ : J * s

lowerCamelCase			:					Union[str, Any]         =	3E8  # unit of c : m * s^-1





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

     if (force, area, distance).count(0       ) != 1:
          raise ValueError("One and only one argument must be 0"       )
     if force < 0:
          raise ValueError("Magnitude of force can not be negative"       )
     if distance < 0:
          raise ValueError("Distance can not be negative"       )
     if area < 0:
          raise ValueError("Area can not be negative"       )
     if force == 0:
          __lowerCAmelCase								=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
              240 * (distance) ** 4
          )
          return {"force": force}
     elif area == 0:
          __lowerCAmelCase								=					(240 * force * (distance) ** 4) / (
              REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
          )
          return {"area": area}
     elif distance == 0:
          __lowerCAmelCase								=					(
              (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
          ) ** (1 / 4)
          return {"distance": distance}
     raise ValueError("One and only one argument must be 0"       )


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()






