


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase     :						int	=     {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						Tuple	=     [
	    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AdaptiveEmbedding',
	    'TransfoXLForSequenceClassification',
	    'TransfoXLLMHeadModel',
	    'TransfoXLModel',
	    'TransfoXLPreTrainedModel',
	    'load_tf_weights_in_transfo_xl',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAdaptiveEmbedding',
	    'TFTransfoXLForSequenceClassification',
	    'TFTransfoXLLMHeadModel',
	    'TFTransfoXLMainLayer',
	    'TFTransfoXLModel',
	    'TFTransfoXLPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
	from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_transfo_xl import (
		    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AdaptiveEmbedding,
		    TransfoXLForSequenceClassification,
		    TransfoXLLMHeadModel,
		    TransfoXLModel,
		    TransfoXLPreTrainedModel,
		    load_tf_weights_in_transfo_xl,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_transfo_xl import (
		    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAdaptiveEmbedding,
		    TFTransfoXLForSequenceClassification,
		    TFTransfoXLLMHeadModel,
		    TFTransfoXLMainLayer,
		    TFTransfoXLModel,
		    TFTransfoXLPreTrainedModel,
		)

else:
	import sys

	__lowercase     :						str	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''



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							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					if is_torch_version('<'						,				'2.0.0'       ) or not hasattr(_SCREAMING_SNAKE_CASE						,				'_dynamo'       ):
										return False
					return isinstance(_SCREAMING_SNAKE_CASE						,				torch._dynamo.eval_frame.OptimizedModule       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       bool = True       ):
					__a      :	int    =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					__a      :	Any    =  is_compiled_module(_SCREAMING_SNAKE_CASE       )
					if is_compiled:
										__a      :	List[Any]    =  model
										__a      :	Union[str, Any]    =  model._orig_mod

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

					while isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Union[str, Any]    =  model.module

					if not keep_fpaa_wrapper:
										__a      :	Optional[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'forward'       )
										__a      :	str    =  model.__dict__.pop('_original_forward'						,				_SCREAMING_SNAKE_CASE       )
										if original_forward is not None:
															while hasattr(_SCREAMING_SNAKE_CASE						,				'__wrapped__'       ):
																				__a      :	Any    =  forward.__wrapped__
																				if forward == original_forward:
																									break
															__a      :	str    =  forward
										if getattr(_SCREAMING_SNAKE_CASE						,				'_converted_to_transformer_engine'						,				_SCREAMING_SNAKE_CASE       ):
															convert_model(_SCREAMING_SNAKE_CASE						,				to_transformer_engine=_SCREAMING_SNAKE_CASE       )

					if is_compiled:
										__a      :	List[str]    =  model
										__a      :	Optional[int]    =  compiled_model

					return model






def   lowerCamelCase							():
					PartialState().wait_for_everyone()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					elif PartialState().local_process_index == 0:
										torch.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )






@contextmanager
def   lowerCamelCase							(**_SCREAMING_SNAKE_CASE				:       Tuple       ):
					for key, value in kwargs.items():
										__a      :	Optional[int]    =  str(_SCREAMING_SNAKE_CASE       )

					yield

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict       ):
					if not hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ) and not hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										__a      :	List[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'__class__'						,				_SCREAMING_SNAKE_CASE       )
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ):
										return obj.__qualname__
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										return obj.__name__
					return str(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					for key, value in source.items():
										if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	int    =  destination.setdefault(_SCREAMING_SNAKE_CASE						,				{}       )
															merge_dicts(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Tuple    =  value

					return destination






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int = None       ):
					if port is None:
										__a      :	List[str]    =  29_500
					with socket.socket(socket.AF_INET						,				socket.SOCK_STREAM       ) as s:
										return s.connect_ex(('localhost', port)       ) == 0



