

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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

							return orig_key





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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

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

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

							return orig_state_dict





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   torch.load(UpperCamelCase__       ,							map_location='cpu'				)['model_state_dict']
							snake_case_          =   YosoConfig.from_json_file(UpperCamelCase__				)
							snake_case_          =   YosoForMaskedLM(UpperCamelCase__				)

							snake_case_          =   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__":
					_UpperCAmelCase		:       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."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None				):







							'''simple docstring'''






							if start is None:
														snake_case_          =   0

							if end is None:
														snake_case_          =   len(UpperCamelCase__				) - 1

							if start >= end:
														return

							snake_case_          =   (start + end) // 2

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							slowsort(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)

							if sequence[end] < sequence[mid]:
														snake_case_    ,   snake_case_          =   sequence[mid], sequence[end]

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							end - 1				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()




