





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


__A				:    Tuple       =   logging.get_logger(__name__)

@dataclass
class 	A_       (a_			):
	UpperCAmelCase__					=			[
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]
	def __init__(    self							,					**_A		):






				'''simple docstring'''







				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										UpperCAmelCase   		=				deprecated_arg[3:]
										setattr(self							,					_A							,					not kwargs.pop(_A		)		)
										logger.warning(
										    F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
										    F""" {positive_arg}={kwargs[positive_arg]}"""		)

				UpperCAmelCase   		=				kwargs.pop('''torchscript'''							,					self.torchscript		)
				UpperCAmelCase   		=				kwargs.pop('''torch_xla_tpu_print_metrics'''							,					self.torch_xla_tpu_print_metrics		)
				UpperCAmelCase   		=				kwargs.pop('''fp16_opt_level'''							,					self.fpaa_opt_level		)
				super().__init__(**_A		)

	UpperCAmelCase__					=			field(default=a_     ,							metadata={'''help''': '''Trace the models using torchscript'''}			)
	UpperCAmelCase__					=			field(default=a_     ,							metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}			)
	UpperCAmelCase__					=			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      _lowercase				(    self		):






				'''simple docstring'''







				requires_backends(self							,					['''torch''']		)
				logger.info('''PyTorch: setting up devices'''		)
				if not self.cuda:
							UpperCAmelCase   		=				torch.device('''cpu'''		)
							UpperCAmelCase   		=				0
				elif is_torch_tpu_available():
							UpperCAmelCase   		=				xm.xla_device()
							UpperCAmelCase   		=				0
				else:
							UpperCAmelCase   		=				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
							UpperCAmelCase   		=				torch.cuda.device_count()
				return device, n_gpu
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return is_torch_tpu_available() and self.tpu
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				requires_backends(self							,					['''torch''']		)
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				requires_backends(self							,					['''torch''']		)
				return self._setup_devices[0]
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				requires_backends(self							,					['''torch''']		)
				return self._setup_devices[1]







	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.n_gpu > 0










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

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

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

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

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

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

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

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

			UpperCAmelCase   		=				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__":
				__A				:    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."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



