



from typing import TYPE_CHECKING

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


__magic_name__       					=      {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ["""CamembertTokenizer"""]

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CamembertForCausalLM""",
							    """CamembertForMaskedLM""",
							    """CamembertForMultipleChoice""",
							    """CamembertForQuestionAnswering""",
							    """CamembertForSequenceClassification""",
							    """CamembertForTokenClassification""",
							    """CamembertModel""",
							    """CamembertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCamembertForCausalLM""",
							    """TFCamembertForMaskedLM""",
							    """TFCamembertForMultipleChoice""",
							    """TFCamembertForQuestionAnswering""",
							    """TFCamembertForSequenceClassification""",
							    """TFCamembertForTokenClassification""",
							    """TFCamembertModel""",
							    """TFCamembertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_camembert import CamembertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_camembert_fast import CamembertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_camembert import (
														    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CamembertForCausalLM,
														    CamembertForMaskedLM,
														    CamembertForMultipleChoice,
														    CamembertForQuestionAnswering,
														    CamembertForSequenceClassification,
														    CamembertForTokenClassification,
														    CamembertModel,
														    CamembertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_camembert import (
														    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCamembertForCausalLM,
														    TFCamembertForMaskedLM,
														    TFCamembertForMultipleChoice,
														    TFCamembertForQuestionAnswering,
														    TFCamembertForSequenceClassification,
														    TFCamembertForTokenClassification,
														    TFCamembertModel,
														    TFCamembertPreTrainedModel,
														)

else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

