



"""simple docstring"""







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Dict    ,		lowerCamelCase_:       Tuple    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						snake_case      : Optional[Any]							=   ConvBertConfig.from_json_file(lowerCamelCase_   )
						snake_case      : List[str]							=   ConvBertModel(lowerCamelCase_   )

						snake_case      : List[Any]							=   load_tf_weights_in_convbert(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )
						model.save_pretrained(lowerCamelCase_   )

						snake_case      : Dict							=   TFConvBertModel.from_pretrained(lowerCamelCase_    ,		from_pt=lowerCamelCase_   )
						tf_model.save_pretrained(lowerCamelCase_   )


if __name__ == "__main__":
			A    =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--convbert_config_file',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        'The config json file corresponding to the pre-trained ConvBERT model. \n'
			        'This specifies the model architecture.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			A    =	parser.parse_args()
			convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)








"""simple docstring"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int    ,		lowerCamelCase_:       bool = False   ):
						"""simple docstring"""






						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(lowerCamelCase_   )
						np.random.seed(lowerCamelCase_   )
						torch.manual_seed(lowerCamelCase_   )
						if is_xpu_available():
												torch.xpu.manual_seed_all(lowerCamelCase_   )
						elif is_npu_available():
												torch.npu.manual_seed_all(lowerCamelCase_   )
						else:
												torch.cuda.manual_seed_all(lowerCamelCase_   )
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[RNGType] = None    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						if rng_type == RNGType.TORCH:
												snake_case      : List[Any]							=   torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												snake_case      : Union[str, Any]							=   torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												snake_case      : Optional[int]							=   torch.tensor(xm.get_rng_state()   )
						elif rng_type == RNGType.NPU:
												assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
												snake_case      : int							=   torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												snake_case      : int							=   torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												snake_case      : List[Any]							=   generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						snake_case      : Dict							=   AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												snake_case      : List[str]							=   rng_state.to(xm.xla_device()   )
												xm.collective_broadcast([rng_state]   )
												xm.mark_step()
												snake_case      : List[Any]							=   rng_state.cpu()
						elif (
						    state.distributed_type in CUDA_DISTRIBUTED_TYPES
						    or state.distributed_type == DistributedType.MULTI_NPU
						    or state.distributed_type == DistributedType.MULTI_XPU
						):
												snake_case      : str							=   rng_state.to(state.device   )
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )
												snake_case      : Union[str, Any]							=   rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )

						# Set the broadcast rng state
						if rng_type == RNGType.TORCH:
												torch.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.CUDA:
												torch.cuda.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.NPU:
												torch.npu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XPU:
												torch.xpu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XLA:
												xm.set_rng_state(rng_state.item()   )
						elif rng_type == RNGType.GENERATOR:
												generator.set_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Union[str, RNGType]]    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						for rng_type in rng_types:
												synchronize_rng_state(RNGType(lowerCamelCase_   )    ,		generator=lowerCamelCase_   )



