&`i3ddlZddlZddlZddlmZmZmZddlZddl Z ddl Z ddl m Z ddl mZmZddlmZmZmZerddlmZejeZe d4deded eefd Zed5d d defdZed6defdZed6defdZGddZ GddZ!GddZ"edZ#ed6defdZ$GddZ%GddZ&Gd d!Z'ed6defd"Z(d#Z)e d7d&eded'ed(ed)ed eed*eed+eed,efd-Z*e d.d/d01d8d2eedefd3Z+dS)9N) TYPE_CHECKINGAnyOptional) Deprecated) DeveloperAPI PublicAPI) TensorShapeTensorStructType TensorType)AlgorithmConfigdata frameworkdevicec|dkrddlm}|||S|dkr*t\}}tjfd|St d|d) auConverts any nested numpy struct into framework-specific tensors. Args: data: The input data (numpy) to convert to framework-specific tensors. framework: The framework to convert to. Only "torch" and "tf2" allowed. device: An optional device name (for torch only). Returns: The converted tensor struct matching the input data. torchr)convert_to_torch_tensor)rtf2c.|SN)convert_to_tensor)stfs m/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/utils/framework.pyz#convert_to_tensor../sB,@,@,C,Cz framework=z( not supported in `convert_to_tensor()`!)ray.rllib.utils.torch_utilsr try_import_tftree map_structureNotImplementedError)r rrr_rs @rrrs GGGGGGG&&tF;;;; e   ??2q!"C"C"C"CTJJJ HYHHH  rconfigr num_gpus_requestedc |jdkrt\}}|dkrddlm}|}t |dkrCt jjt jjj kr|dS|j |j ksJd|j d| |j S| dStd|jd ) aReturns a single device (CPU or some GPU) depending on a config. Args: config: An AlgorithmConfig to extract information from about the device to use. num_gpus_requested: The number of GPUs actually requested. This may be the value of `config.num_gpus_per_env_runner` when for example calling this function from an EnvRunner. Returns: A single device (or name) given `config` and `num_gpus_requested`. rr) get_devicesr"zlocal_gpu_idx z+ is not a valid GPU ID or is not available.cpuz`framework_str` z not supported!) framework_strtry_import_torchray.air._internal.torch_utilsr&lenray_privateworker_mode WORKER_MODE local_gpu_idxcuda device_countrr )r#r$rr!r&devicess r get_devicer55sw&&#%%q  ! ! A A A A A A "kmmGG !!L'--//3<3F3RRRqz!+ej.E.E.G.GGGG+V%9+++HGG||F$8999<<&& &! Dv3 D D D   rFerrorcdtjvrtddS ddl}ddl}n"#t $r|rt dYdSwxYw||fS)a*Tries importing JAX and FLAX and returns both modules (or Nones). Args: error: Whether to raise an error if JAX/FLAX cannot be imported. Returns: Tuple containing the jax- and the flax modules. Raises: ImportError: If error=True and JAX is not installed. RLLIB_TEST_NO_JAX_IMPORTz$Not importing JAX for test purposes.)NNrNzCould not import JAX! RLlib requires you to install at least one deep-learning framework: `pip install [torch|tensorflow|jax]`.)osenvironloggerwarningflaxjax ImportError)r6r=r>s rtry_import_jaxr@is"RZ//=>>>z     8  zz 9s5AAct}dtjvrtdd|dfSdtjvrdtjd<d}dt jvrt jd}d}n, d dl}n&#t$r|rtd d|dfcYSwxYw |j j }|j |j j |s(|||j |j jn#t"$r|}YnwxYwt%|d sd }nd |jddvrdnd }|||fS)aTries importing tf and returns the module (or None). Args: error: Whether to raise an error if tf cannot be imported. Returns: Tuple containing 1) tf1.x module (either from tf2.x.compat.v1 OR as tf1.x). 2) tf module (resulting from `import tensorflow`). Either tf1.x or 2.x. 3) The actually installed tf version as int: 1 or 2. Raises: ImportError: If error=True and tf is not installed. RLLIB_TEST_NO_TF_IMPORTz*Not importing TensorFlow for test purposesNTF_CPP_MIN_LOG_LEVEL3F tensorflowTrzCould not import TensorFlow! RLlib requires you to install at least one deep-learning framework: `pip install [torch|tensorflow|jax]`. __version__r"z2.)_TFStubr9r:r;r<sysmodulesrEr?compatv1logging set_verbosityERRORdisable_v2_behaviorenable_resource_variablesWARNAttributeErrorhasattrrF)r6tf_stub was_imported tf_module tf1_moduleversions rrrs iiG!BJ..CDDDWd""RZ//-0 )* Ls{""K -   ' * * * * * ' ' ' !< $& & & & ' %( (();)ABBB 3  * * , , ,  0 0 2 2 2(();)@AAAA   9m , ,@y4RaR888!!a y' ))s%?B B'&B'+A>D** D98D9ceZdZddZdZdS)rHreturnNc,t|_dSr) _KerasStubkerasselfs r__init__z_TFStub.__init__s\\ rcdS)NFr_s r__bool__z_TFStub.__bool__surr[N)__name__ __module__ __qualname__rardrcrrrHrHs7""""rrHceZdZddZdS)r]r[Nct|_dSr)_FakeTfClassStubModelr_s rraz_KerasStub.__init__s % rrerfrgrhrarcrrr]r]s(&&&&&&rr]ceZdZdZdS)rkc td)Nz:Could not import `tensorflow`. Try pip install tensorflow.r?r`akws rraz_FakeTfClassStub.__init__sVWWWrNrmrcrrrkrks(XXXXXrrkcfd}|S)zqConditional decorator for @tf.function. Use @tf_function(tf) instead to avoid errors if tf is not installed.c^r|S|Sr)executing_eagerlyfunction)funcrWs r decoratorztf_function..decorators3   ; ; = = K!!$'''rrc)rWrys` r tf_functionrzs$((((( rcdtjvrtddS ddl}|S#t $r}|r|Yd}~dSd}~wwxYw)zTries importing tfp and returns the module (or None). Args: error: Whether to raise an error if tfp cannot be imported. Returns: The tfp module. Raises: ImportError: If error=True and tfp is not installed. rBz7Not importing TensorFlow Probability for test purposes.Nr)r9r:r;r<tensorflow_probabilityr?)r6tfpes rtry_import_tfprsx!BJ..PQQQt,,,,   Gttttts2 A AA ceZdZdZdS)_NNStubcRd|_t|_t|_dSr) functional_FakeTorchClassStubModule _ParallelStubparallelrqs rraz_NNStub.__init__s) % rNrmrcrrrrs#(((((rrceZdZdZdS)rc td)Nz0Could not import `torch`. Try pip install torch.rprqs rraz_FakeTorchClassStub.__init__sLMMMrNrmrcrrrrs(NNNNNrrceZdZdZdS)rc6t|_t|_dSr)r DataParallelDistributedDataParallelrqs rraz_ParallelStub.__init__s/':$$$rNrmrcrrrrs#;;;;;rrcdtjvr(tdt S ddl}ddlm}||fS#t$r"|rtdt cYSwxYw)a&Tries importing torch and returns the module (or None). Args: error: Whether to raise an error if torch cannot be imported. Returns: Tuple consisting of the torch- AND torch.nn modules. Raises: ImportError: If error=True and PyTorch is not installed. RLLIB_TEST_NO_TORCH_IMPORTz(Not importing PyTorch for test purposes.rNzCould not import PyTorch! RLlib requires you to install at least one deep-learning framework: `pip install [torch|tensorflow|jax]`.) r9r:r;r< _torch_stubsrtorch.nnnnr?)r6rrs rr)r)"s$rz11ABBB~~  by   8  ~~s A)A21A2c&t}d|fSr)r)rs rrrBs B 8Orrunnamed-variablevalue trainabletf_name torch_tensorshapedtyper[c|dvrqddl}|pHt|dt|tr|jnt|t r|jnd}|jjj |f|||d|ind|iS|dkr|durt\} } t|tj stj |}| |} || jtjfvr| } nS|| jtjfvr| } n)|| jtjfvr| } |r| |} || _| S|S) aCreates a tf variable, a torch tensor, or a python primitive. Args: value: The initial value to use. In the non-tf case, this will be returned as is. In the tf case, this could be a tf-Initializer object. framework: One of "tf", "torch", or None. trainable: Whether the generated variable should be trainable (tf)/require_grad (torch) or not (default: False). tf_name: For framework="tf": An optional name for the tf.Variable. torch_tensor: For framework="torch": Whether to actually create a torch.tensor, or just a python value (default). device: An optional torch device to use for the created torch tensor. shape: An optional shape to use iff `value` does not have any (e.g. if it's an initializer w/o explicit value). dtype: An optional dtype to use iff `value` does not have any (e.g. if it's an initializer w/o explicit value). This should always be a numpy dtype (e.g. np.float32, np.int64). 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