





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[int]:

						"""simple docstring"""





						if "model" in orig_key:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   orig_key.replace('''model.'''       ,			''''''					)
						if "norm1" in orig_key:
												__SCREAMING_SNAKE_CASE:				List[str]           =   orig_key.replace('''norm1'''       ,			'''attention.output.LayerNorm'''					)
						if "norm2" in orig_key:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   orig_key.replace('''norm2'''       ,			'''output.LayerNorm'''					)
						if "norm" in orig_key:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   orig_key.replace('''norm'''       ,			'''LayerNorm'''					)
						if "transformer" in orig_key:
												__SCREAMING_SNAKE_CASE:				int           =   orig_key.split('''.'''					)[0].split('''_'''					)[-1]
												__SCREAMING_SNAKE_CASE:				int           =   orig_key.replace(F"""transformer_{layer_num}"""       ,			F"""encoder.layer.{layer_num}"""					)
						if "mha.attn" in orig_key:
												__SCREAMING_SNAKE_CASE:				Tuple           =   orig_key.replace('''mha.attn'''       ,			'''attention.self'''					)
						if "mha" in orig_key:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   orig_key.replace('''mha'''       ,			'''attention'''					)
						if "W_q" in orig_key:
												__SCREAMING_SNAKE_CASE:				List[str]           =   orig_key.replace('''W_q'''       ,			'''self.query'''					)
						if "W_k" in orig_key:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   orig_key.replace('''W_k'''       ,			'''self.key'''					)
						if "W_v" in orig_key:
												__SCREAMING_SNAKE_CASE:				Dict           =   orig_key.replace('''W_v'''       ,			'''self.value'''					)
						if "ff1" in orig_key:
												__SCREAMING_SNAKE_CASE:				str           =   orig_key.replace('''ff1'''       ,			'''intermediate.dense'''					)
						if "ff2" in orig_key:
												__SCREAMING_SNAKE_CASE:				List[str]           =   orig_key.replace('''ff2'''       ,			'''output.dense'''					)
						if "ff" in orig_key:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   orig_key.replace('''ff'''       ,			'''output.dense'''					)
						if "mlm_class" in orig_key:
												__SCREAMING_SNAKE_CASE:				List[str]           =   orig_key.replace('''mlm.mlm_class'''       ,			'''cls.predictions.decoder'''					)
						if "mlm" in orig_key:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   orig_key.replace('''mlm'''       ,			'''cls.predictions.transform'''					)
						if "cls" not in orig_key:
												__SCREAMING_SNAKE_CASE:				int           =   '''yoso.''' + orig_key

						return orig_key






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Union[str, Any]					)      ->    Optional[int]:

						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE:				Dict           =   orig_state_dict.pop(UpperCamelCase__					)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		__SCREAMING_SNAKE_CASE:				int           =   val

						__SCREAMING_SNAKE_CASE:				List[str]           =   orig_state_dict['''cls.predictions.decoder.bias''']
						__SCREAMING_SNAKE_CASE:				str           =   torch.arange(UpperCamelCase__					).expand((1, -1)					) + 2

						return orig_state_dict






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Union[str, Any]					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)['''model_state_dict''']
						__SCREAMING_SNAKE_CASE:				str           =   YosoConfig.from_json_file(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   YosoForMaskedLM(UpperCamelCase__					)

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   convert_checkpoint_helper(config.max_position_embeddings       ,			UpperCamelCase__					)

						print(model.load_state_dict(UpperCamelCase__					)					)
						model.eval()
						model.save_pretrained(UpperCamelCase__					)

						print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""					)


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The json file for YOSO model config.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCAmelCase							:       Any					 =	parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










import re






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    bool:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   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(UpperCamelCase__       ,			UpperCamelCase__					)					)


if __name__ == "__main__":
							lowerCAmelCase							:       str					 =	"""0094702343221"""

							print(is_sri_lankan_phone_number(phone))



