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 	__A		(      _A       ):



 """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 	__A		(      _A				,					_A = True       ):



 """simple docstring"""


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

 __a							=	is_compiled_module(_A       )
 if is_compiled:
  __a							=	model
  __a							=	model._orig_mod

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

 while isinstance(_A				,					_A       ):
  __a							=	model.module

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

 if is_compiled:
  __a							=	model
  __a							=	compiled_model

 return model



def 	__A		(      ):



 """simple docstring"""


 PartialState().wait_for_everyone()



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


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



@contextmanager
def 	__A		(      **_A       ):



 """simple docstring"""


 for key, value in kwargs.items():
  __a							=	str(_A       )

 yield

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



def 	__A		(      _A       ):



 """simple docstring"""


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



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


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

 return destination






def 	__A		(      _A = None       ):



 """simple docstring"""


 if port is None:
  __a							=	2_9500
 with socket.socket(socket.AF_INET				,					socket.SOCK_STREAM       ) as s:
  return s.connect_ex(("localhost", port)       ) == 0



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


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


