
def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					[]
							for data in source_data:
														for i, el in enumerate(UpperCAmelCase__       ):
																					if len(UpperCAmelCase__       ) < i + 1:
																												data_lists.append([]       )
																					data_lists[i].append(float(UpperCAmelCase__       )       )
							return data_lists



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]			,				UpperCAmelCase__				:      list[int]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					[]
							for dlist, weight in zip(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					min(UpperCAmelCase__       )
														lowerCamelCase_							  =					max(UpperCAmelCase__       )

														lowerCamelCase_							  =					[]
														# for weight 0 score is 1 - actual score
														if weight == 0:
																					for item in dlist:
																												try:
																																			score.append(1 - ((item - mind) / (maxd - mind))       )
																												except ZeroDivisionError:
																																			score.append(1       )

														elif weight == 1:
																					for item in dlist:
																												try:
																																			score.append((item - mind) / (maxd - mind)       )
																												except ZeroDivisionError:
																																			score.append(0       )

        # weight not 0 or 1
														else:
																					lowerCamelCase_							  =					F'''Invalid weight of {weight:f} provided'''
																					raise ValueError(UpperCAmelCase__       )

														score_lists.append(UpperCAmelCase__       )

							return score_lists



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]       )  ->    list[float]:
							lowerCamelCase_							  =					[0 for i in range(len(score_lists[0]       )       )]

							for slist in score_lists:
														for j, ele in enumerate(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					final_scores[j] + ele

							return final_scores



def      __lowerCAmelCase						(							UpperCAmelCase__				:      list[list[float]]			,				UpperCAmelCase__				:      list[int]       )  ->    list[list[float]]:
							lowerCamelCase_							  =					get_data(UpperCAmelCase__       )
							lowerCamelCase_							  =					calculate_each_score(UpperCAmelCase__			,				UpperCAmelCase__       )
							lowerCamelCase_							  =					generate_final_scores(UpperCAmelCase__       )

							# append scores to source data
							for i, ele in enumerate(UpperCAmelCase__       ):
														source_data[i].append(UpperCAmelCase__       )

							return source_data







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						(							UpperCAmelCase__				:      Any       )  ->    List[str]:
							if is_torch_version("""<"""			,				"""2.0.0"""       ) or not hasattr(UpperCAmelCase__			,				"""_dynamo"""       ):
														return False
							return isinstance(UpperCAmelCase__			,				torch._dynamo.eval_frame.OptimizedModule       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool = True       )  ->    str:
							lowerCamelCase_							  =					(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

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

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

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

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

							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					compiled_model

							return model



def      __lowerCAmelCase						(							)  ->    Any:
							PartialState().wait_for_everyone()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Tuple       )  ->    Dict:
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(UpperCAmelCase__			,				UpperCAmelCase__       )
							elif PartialState().local_process_index == 0:
														torch.save(UpperCAmelCase__			,				UpperCAmelCase__       )



@contextmanager
def      __lowerCAmelCase						(							**UpperCAmelCase__				:      Any       )  ->    Dict:
							for key, value in kwargs.items():
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

							yield

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    str:
							if not hasattr(UpperCAmelCase__			,				"""__qualname__"""       ) and not hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""__class__"""			,				UpperCAmelCase__       )
							if hasattr(UpperCAmelCase__			,				"""__qualname__"""       ):
														return obj.__qualname__
							if hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														return obj.__name__
							return str(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:
							for key, value in source.items():
														if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
																					lowerCamelCase_							  =					destination.setdefault(UpperCAmelCase__			,				{}       )
																					merge_dicts(UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					lowerCamelCase_							  =					value

							return destination



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = None       )  ->    bool:
							if port is None:
														lowerCamelCase_							  =					2_9_5_0_0
							with socket.socket(socket.AF_INET			,				socket.SOCK_STREAM       ) as s:
														return s.connect_ex(("""localhost""", port)       ) == 0





