



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
		import torch

if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


_lowercase			:			Dict										=						logging.get_logger(__name__)

@dataclass
class 	__SCREAMING_SNAKE_CASE     (				lowerCamelCase_       ):


					'''simple docstring'''





					_a             =							[
					    '''no_inference''',
					    '''no_cuda''',
					    '''no_tpu''',
					    '''no_speed''',
					    '''no_memory''',
					    '''no_env_print''',
					    '''no_multi_process''',
					]



					def __init__(	self							:  Any,  **lowerCamelCase							:  List[str]					)-> Tuple:
								for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
														lowerCamelCase__		:		List[str]		     =deprecated_arg[3:]
														setattr(self,  __snake_case,  not kwargs.pop(__snake_case					)					)
														logger.warning(
														    F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
														    F''' {positive_arg}={kwargs[positive_arg]}'''					)

								lowerCamelCase__		:		Optional[int]		     =kwargs.pop('''torchscript''',  self.torchscript					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''torch_xla_tpu_print_metrics''',  self.torch_xla_tpu_print_metrics					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''fp16_opt_level''',  self.fpaa_opt_level					)
								super().__init__(**__snake_case					)

					_a             =							field(default=lowerCamelCase_		,       metadata={'help': 'Trace the models using torchscript'}       )
					_a             =							field(default=lowerCamelCase_		,       metadata={'help': 'Print Xla/PyTorch tpu metrics'}       )
					_a             =							field(
					    default='O1'		,       metadata={
					        'help': (
					            'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
					            'See details at https://nvidia.github.io/apex/amp.html'
					        )
					    }		,       )



					@cached_property
					def 		snake_case	(	self							:  Optional[Any]					)-> Tuple["torch.device", int]:
								requires_backends(self,  ['''torch''']					)
								logger.info('''PyTorch: setting up devices'''					)
								if not self.cuda:
											lowerCamelCase__		:		str		     =torch.device('''cpu'''					)
											lowerCamelCase__		:		str		     =0
								elif is_torch_tpu_available():
											lowerCamelCase__		:		Union[str, Any]		     =xm.xla_device()
											lowerCamelCase__		:		Optional[int]		     =0
								else:
											lowerCamelCase__		:		str		     =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)
											lowerCamelCase__		:		List[Any]		     =torch.cuda.device_count()
								return device, n_gpu



					@property
					def 		snake_case	(	self							:  List[Any]					)-> Union[str, Any]:
								return is_torch_tpu_available() and self.tpu



					@property
					def 		snake_case	(	self							:  List[Any]					)-> int:
								requires_backends(self,  ['''torch''']					)
								# TODO(PVP): currently only single GPU is supported
								return torch.cuda.current_device()



					@property
					def 		snake_case	(	self							:  List[str]					)-> "torch.device":
								requires_backends(self,  ['''torch''']					)
								return self._setup_devices[0]



					@property
					def 		snake_case	(	self							:  int					)-> List[str]:
								requires_backends(self,  ['''torch''']					)
								return self._setup_devices[1]




					@property
					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								return self.n_gpu > 0









"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =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__":
		_lowercase			:			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."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




