


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__				):
			if is_torch_version('<',      '2.0.0'				) or not hasattr(_lowerCAmelCase,      '_dynamo'				):
						return False
			return isinstance(_lowerCAmelCase,      torch._dynamo.eval_frame.OptimizedModule				)






def 						a__			(				A__,      A__ = True				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

			SCREAMING_SNAKE_CASE_		:     Any					=					is_compiled_module(_lowerCAmelCase				)
			if is_compiled:
						SCREAMING_SNAKE_CASE_		:     int					=					model
						SCREAMING_SNAKE_CASE_		:     int					=					model._orig_mod

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

			while isinstance(_lowerCAmelCase,      _lowerCAmelCase				):
						SCREAMING_SNAKE_CASE_		:     List[str]					=					model.module

			if not keep_fpaa_wrapper:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					getattr(_lowerCAmelCase,      'forward'				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					model.__dict__.pop('_original_forward',      _lowerCAmelCase				)
						if original_forward is not None:
									while hasattr(_lowerCAmelCase,      '__wrapped__'				):
												SCREAMING_SNAKE_CASE_		:     Tuple					=					forward.__wrapped__
												if forward == original_forward:
															break
									SCREAMING_SNAKE_CASE_		:     str					=					forward
						if getattr(_lowerCAmelCase,      '_converted_to_transformer_engine',      _lowerCAmelCase				):
									convert_model(_lowerCAmelCase,      to_transformer_engine=_lowerCAmelCase				)

			if is_compiled:
						SCREAMING_SNAKE_CASE_		:     str					=					model
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					compiled_model

			return model






def 						a__			(				):
			PartialState().wait_for_everyone()






def 						a__			(				A__,      A__				):
			if PartialState().distributed_type == DistributedType.TPU:
						xm.save(_lowerCAmelCase,      _lowerCAmelCase				)
			elif PartialState().local_process_index == 0:
						torch.save(_lowerCAmelCase,      _lowerCAmelCase				)






@contextmanager
def 						a__			(				**A__				):
			for key, value in kwargs.items():
						SCREAMING_SNAKE_CASE_		:     List[str]					=					str(_lowerCAmelCase				)

			yield

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






def 						a__			(				A__				):
			if not hasattr(_lowerCAmelCase,      '__qualname__'				) and not hasattr(_lowerCAmelCase,      '__name__'				):
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					getattr(_lowerCAmelCase,      '__class__',      _lowerCAmelCase				)
			if hasattr(_lowerCAmelCase,      '__qualname__'				):
						return obj.__qualname__
			if hasattr(_lowerCAmelCase,      '__name__'				):
						return obj.__name__
			return str(_lowerCAmelCase				)






def 						a__			(				A__,      A__				):
			for key, value in source.items():
						if isinstance(_lowerCAmelCase,      _lowerCAmelCase				):
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					destination.setdefault(_lowerCAmelCase,      {}				)
									merge_dicts(_lowerCAmelCase,      _lowerCAmelCase				)
						else:
									SCREAMING_SNAKE_CASE_		:     Dict					=					value

			return destination






def 						a__			(				A__ = None				):
			if port is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					2_9_5_0_0
			with socket.socket(socket.AF_INET,      socket.SOCK_STREAM				) as s:
						return s.connect_ex(('localhost', port)				) == 0







"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
