






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def   a			(						lowerCamelCase_				):






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

				return orig_key






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase__    =			orig_state_dict.pop(lowerCamelCase_				)

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

				lowercase__    =			orig_state_dict['''cls.predictions.decoder.bias''']
				lowercase__    =			torch.arange(lowerCamelCase_				).expand((1, -1)				) + 2

				return orig_state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			torch.load(lowerCamelCase_		,   map_location='''cpu'''				)['''model_state_dict''']
				lowercase__    =			YosoConfig.from_json_file(lowerCamelCase_				)
				lowercase__    =			YosoForMaskedLM(lowerCamelCase_				)

				lowercase__    =			convert_checkpoint_helper(config.max_position_embeddings		,   lowerCamelCase_				)

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

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


if __name__ == "__main__":
							A__	:			Optional[int]			   = 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.'
							)
							A__	:			Any			   = parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__	:			Optional[int]			   = {}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Optional[int]			   = ['GPTSw3Tokenizer']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							A__	:			Optional[int]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




