





"""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 			__snake_case						(      __A ,__A = False    )		->				Union[str, Any]:
   if device_specific:
      seed += AcceleratorState().process_index
   random.seed(__A    )
   np.random.seed(__A    )
   torch.manual_seed(__A    )
   if is_xpu_available():
      torch.xpu.manual_seed_all(__A    )
   elif is_npu_available():
      torch.npu.manual_seed_all(__A    )
   else:
      torch.cuda.manual_seed_all(__A    )
   # ^^ safe to call this function even if cuda is not available
   if is_tpu_available():
      xm.set_rng_state(__A    )




def 			__snake_case						(      __A = None ,__A = None    )		->				Dict:
   # Get the proper rng state
   if rng_type == RNGType.TORCH:
      lowercase    :			Tuple  =       torch.get_rng_state()
   elif rng_type == RNGType.CUDA:
      lowercase    :			int  =       torch.cuda.get_rng_state()
   elif rng_type == RNGType.XLA:
      assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
      lowercase    :			Any  =       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."
      lowercase    :			List[str]  =       torch.npu.get_rng_state()
   elif rng_type == RNGType.XPU:
      assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
      lowercase    :			Dict  =       torch.xpu.get_rng_state()
   elif rng_type == RNGType.GENERATOR:
      assert generator is not None, "Need a generator to synchronize its seed."
      lowercase    :			Optional[Any]  =       generator.get_state()

   # Broadcast the rng state from device 0 to other devices
   lowercase    :			List[Any]  =       AcceleratorState()
   if state.distributed_type == DistributedType.TPU:
      lowercase    :			Dict  =       rng_state.to(xm.xla_device()    )
      xm.collective_broadcast([rng_state]    )
      xm.mark_step()
      lowercase    :			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
   ):
      lowercase    :			Optional[Any]  =       rng_state.to(state.device    )
      torch.distributed.broadcast(__A ,0    )
      lowercase    :			Optional[int]  =       rng_state.cpu()
   elif state.distributed_type == DistributedType.MULTI_CPU:
      torch.distributed.broadcast(__A ,0    )

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







def 			__snake_case						(      __A ,__A = None    )		->				Optional[int]:
   for rng_type in rng_types:
      synchronize_rng_state(RNGType(__A    ) ,generator=__A    )









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


