





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


						'''simple docstring'''


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

						return orig_key



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Dict:


						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE	=		orig_state_dict.pop(_SCREAMING_SNAKE_CASE			)

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

						SCREAMING_SNAKE_CASE	=		orig_state_dict["""cls.predictions.decoder.bias"""]
						SCREAMING_SNAKE_CASE	=		torch.arange(_SCREAMING_SNAKE_CASE			).expand((1, -1)			) + 2

						return orig_state_dict



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     List[Any]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE	=		torch.load(_SCREAMING_SNAKE_CASE				,		map_location="""cpu"""			)["""model_state_dict"""]
						SCREAMING_SNAKE_CASE	=		YosoConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
						SCREAMING_SNAKE_CASE	=		YosoForMaskedLM(_SCREAMING_SNAKE_CASE			)

						SCREAMING_SNAKE_CASE	=		convert_checkpoint_helper(config.max_position_embeddings				,		_SCREAMING_SNAKE_CASE			)

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

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE_		     = 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."""
						)
						SCREAMING_SNAKE_CASE_		     = parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


