




def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   list[int]	):




 """simple docstring"""







 if not len(_lowerCAmelCase	) == len(_lowerCAmelCase	) == 3:
  raise ValueError("Please enter a valid equation."	)
 if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
  raise ValueError("Both a & b of two equations can't be zero."	)

 # Extract the coefficients
 UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						equationa
 UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						equationa

 # Calculate the determinants of the matrices
 UpperCAmelCase__							 =						aa * ba - aa * ba
 UpperCAmelCase__							 =						ca * ba - ca * ba
 UpperCAmelCase__							 =						aa * ca - aa * ca

 # Check if the system of linear equations has a solution (using Cramer's rule)
 if determinant == 0:
  if determinant_x == determinant_y == 0:
   raise ValueError("Infinite solutions. (Consistent system)"	)
  else:
   raise ValueError("No solution. (Inconsistent system)"	)
 else:
  if determinant_x == determinant_y == 0:
   # Trivial solution (Inconsistent system)
   return (0.0, 0.0)
  else:
   UpperCAmelCase__							 =						determinant_x / determinant
   UpperCAmelCase__							 =						determinant_y / determinant
   # Non-Trivial Solution (Consistent system)
   return (x, y)











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						:   Union[str, 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						:   str		=   ["BertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "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						:   Dict		=   [
      "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						:   Optional[Any]		=   [
      "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__)





