import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

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




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      if is_torch_version("<",  "2.0.0"							) or not hasattr(__A,  "_dynamo"							):
            return False
      return isinstance(__A,  torch._dynamo.eval_frame.OptimizedModule							)




def    lowerCAmelCase_ (     __A,  __A = True							)     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

      UpperCAmelCase__								=  is_compiled_module(__A							)
      if is_compiled:
            UpperCAmelCase__								=  model
            UpperCAmelCase__								=  model._orig_mod

      if is_deepspeed_available():
            options += (DeepSpeedEngine,)

      while isinstance(__A,  __A							):
            UpperCAmelCase__								=  model.module

      if not keep_fpaa_wrapper:
            UpperCAmelCase__								=  getattr(__A,  "forward"							)
            UpperCAmelCase__								=  model.__dict__.pop("_original_forward",  __A							)
            if original_forward is not None:
                  while hasattr(__A,  "__wrapped__"							):
                        UpperCAmelCase__								=  forward.__wrapped__
                        if forward == original_forward:
                              break
                  UpperCAmelCase__								=  forward
            if getattr(__A,  "_converted_to_transformer_engine",  __A							):
                  convert_model(__A,  to_transformer_engine=__A							)

      if is_compiled:
            UpperCAmelCase__								=  model
            UpperCAmelCase__								=  compiled_model

      return model




def    lowerCAmelCase_ (     )     ->				Dict:







      '''simple docstring'''


      PartialState().wait_for_everyone()




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


      if PartialState().distributed_type == DistributedType.TPU:
            xm.save(__A,  __A							)
      elif PartialState().local_process_index == 0:
            torch.save(__A,  __A							)




@contextmanager
def    lowerCAmelCase_ (     **__A							)     ->				Optional[int]:







      '''simple docstring'''


      for key, value in kwargs.items():
            UpperCAmelCase__								=  str(__A							)

      yield

      for key in kwargs:
            if key.upper() in os.environ:
                  del os.environ[key.upper()]




def    lowerCAmelCase_ (     __A							)     ->				Tuple:







      '''simple docstring'''


      if not hasattr(__A,  "__qualname__"							) and not hasattr(__A,  "__name__"							):
            UpperCAmelCase__								=  getattr(__A,  "__class__",  __A							)
      if hasattr(__A,  "__qualname__"							):
            return obj.__qualname__
      if hasattr(__A,  "__name__"							):
            return obj.__name__
      return str(__A							)




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


      for key, value in source.items():
            if isinstance(__A,  __A							):
                  UpperCAmelCase__								=  destination.setdefault(__A,  {}							)
                  merge_dicts(__A,  __A							)
            else:
                  UpperCAmelCase__								=  value

      return destination







def    lowerCAmelCase_ (     __A = None							)     ->				bool:







      '''simple docstring'''


      if port is None:
            UpperCAmelCase__								=  29_500
      with socket.socket(socket.AF_INET,  socket.SOCK_STREAM							) as s:
            return s.connect_ex(("localhost", port)							) == 0











import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Optional[Any]			     = {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'mvp'
      a  : Optional[Any]							       =	['past_key_values']
      a  : int							       =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(      self					:			Dict		,      __lowercase					:			Tuple=50267		,      __lowercase					:			Optional[int]=1024		,      __lowercase					:			Any=12		,      __lowercase					:			List[Any]=4096		,      __lowercase					:			Optional[Any]=16		,      __lowercase					:			List[str]=12		,      __lowercase					:			Optional[Any]=4096		,      __lowercase					:			Tuple=16		,      __lowercase					:			Union[str, Any]=0.0		,      __lowercase					:			str=0.0		,      __lowercase					:			Any="gelu"		,      __lowercase					:			Any=1024		,      __lowercase					:			List[str]=0.1		,      __lowercase					:			List[Any]=0.0		,      __lowercase					:			int=0.0		,      __lowercase					:			str=0.02		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Union[str, Any]=False		,      __lowercase					:			Dict=True		,      __lowercase					:			List[Any]=1		,      __lowercase					:			Optional[Any]=0		,      __lowercase					:			Union[str, Any]=2		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Dict=2		,      __lowercase					:			int=2		,      __lowercase					:			Union[str, Any]=False		,      __lowercase					:			Union[str, Any]=100		,      __lowercase					:			str=800		,      **__lowercase					:			Union[str, Any]		,      )							->      Any:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Union[str, Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	int           =  encoder_ffn_dim
        __UpperCAmelCase							:	Tuple           =  encoder_layers
        __UpperCAmelCase							:	List[str]           =  encoder_attention_heads
        __UpperCAmelCase							:	Any           =  decoder_ffn_dim
        __UpperCAmelCase							:	List[Any]           =  decoder_layers
        __UpperCAmelCase							:	str           =  decoder_attention_heads
        __UpperCAmelCase							:	Optional[int]           =  dropout
        __UpperCAmelCase							:	Tuple           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	List[Any]           =  activation_function
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	Any           =  encoder_layerdrop
        __UpperCAmelCase							:	Union[str, Any]           =  decoder_layerdrop
        __UpperCAmelCase							:	List[Any]           =  classifier_dropout
        __UpperCAmelCase							:	Optional[int]           =  use_cache
        __UpperCAmelCase							:	int           =  encoder_layers
        __UpperCAmelCase							:	Union[str, Any]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Tuple           =  use_prompt
        __UpperCAmelCase							:	str           =  prompt_length
        __UpperCAmelCase							:	Union[str, Any]           =  prompt_mid_dim

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      decoder_start_token_id=__lowercase		,      forced_eos_token_id=__lowercase		,      **__lowercase		,      )

        if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""		,      __lowercase					):
          __UpperCAmelCase							:	Optional[Any]           =  self.bos_token_id
          warnings.warn(
              f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
              """The config can simply be saved and uploaded again to be fixed."""					)


