


"""simple docstring"""




import re

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bool:
		__a           =			re.compile(
		    r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$'''			)

		return bool(re.search(lowerCAmelCase__    ,					lowerCAmelCase__			)			)


if __name__ == "__main__":
				lowercase_			     =  "0094702343221"

				print(is_sri_lankan_phone_number(phone))










"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

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






