
    &`i                        d dl Z d dlZd dlmZmZ d dlmZmZ d dlZ	d dl
mZ d dlmZ d dlmZ d dlmZmZ  e            \  ZZZ e j        e          Ze G d d	e
                      Z G d dej                  ZdS )    N)ABCMetaabstractmethod)DictList)MultiAgentBatch)	PublicAPI)try_import_tf)SampleBatchType
TensorTypec                   p    e Zd ZdZeedefd                        Zeddede	e
ef         fd            ZdS )	InputReaderzFAPI for collecting and returning experiences during policy evaluation.returnc                     t           )zReturns the next batch of read experiences.

        Returns:
            The experience read (SampleBatch or MultiAgentBatch).
        )NotImplementedError)selfs    r/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/offline/input_reader.pynextzInputReader.next   s
     "!       
queue_sizec                 "   t          | d          rt          d          t                              d           |                                 t          t                    rt          d          fdt          	                                          D             }fd|D             }d fd|D             D             t                              |||	          }|                                }t                              d
                    |                      t          | |||          | _        | j                                       | j                                         fd|                                D             }|S )a`  Returns TensorFlow queue ops for reading inputs from this reader.

        The main use of these ops is for integration into custom model losses.
        For example, you can use tf_input_ops() to read from files of external
        experiences to add an imitation learning loss to your model.

        This method creates a queue runner thread that will call next() on this
        reader repeatedly to feed the TensorFlow queue.

        Args:
            queue_size: Max elements to allow in the TF queue.

        .. testcode::
            :skipif: True

            from ray.rllib.models.modelv2 import ModelV2
            from ray.rllib.offline.json_reader import JsonReader
            imitation_loss = ...
            class MyModel(ModelV2):
                def custom_loss(self, policy_loss, loss_inputs):
                    reader = JsonReader(...)
                    input_ops = reader.tf_input_ops()
                    logits, _ = self._build_layers_v2(
                        {"obs": input_ops["obs"]},
                        self.num_outputs, self.options)
                    il_loss = imitation_loss(logits, input_ops["action"])
                    return policy_loss + il_loss

        You can find a runnable version of this in examples/custom_loss.py.

        Returns:
            Dict of Tensors, one for each column of the read SampleBatch.
        _queue_runnerzfA queue runner already exists for this input reader. You can only call tf_input_ops() once per reader.z0Reading initial batch of data from input reader.z9tf_input_ops() is not implemented for multi agent batchesc                     g | ]@}t          j        t          j        |                   j        t           j                  >|AS  )np
issubdtypearraydtypenumber.0kbatchs     r   
<listcomp>z,InputReader.tf_input_ops.<locals>.<listcomp>U   sN     
 
 
}RXeAh//5ryAA

 
 
r   c                 *    g | ]}|         j         S r   )r   r    s     r   r$   z,InputReader.tf_input_ops.<locals>.<listcomp>Z   s    ///Q%(.///r   c                 0    i | ]\  }}|d |dd         z   S ))r   Nr   )r!   r"   ss      r   
<dictcomp>z,InputReader.tf_input_ops.<locals>.<dictcomp>[   s*    UUUv1!UQqrrU]UUUr   c                 .    g | ]}||         j         fS r   )shaper    s     r   r$   z,InputReader.tf_input_ops.<locals>.<listcomp>[   s$    1T1T1T!1eAhn2E1T1T1Tr   )capacitydtypesnameszCreating TF queue runner for {}c                 Z    i | ]'\  }}|t                               ||                   (S r   )tfreshape)r!   r"   tshapess      r   r)   z,InputReader.tf_input_ops.<locals>.<dictcomp>d   s1    GGGtq!q"**Qq	**GGGr   )hasattr
ValueErrorloggerinfor   
isinstancer   r   sortedkeystf1	FIFOQueuedequeueformat_QueueRunnerr   enqueuestartitems)	r   r   r:   r-   queuetensorsoutr#   r3   s	          @@r   tf_input_opszInputReader.tf_input_ops    s   H 4)) 	D  
 	FGGG		e_-- 	%K  
 
 
 
EJJLL))
 
 

 0///$///UU1T1T1T1Tt1T1T1TUUUz&MM--//5<<TBBCCC)$tVDD""5)))  """GGGGw}}GGG
r   N)r   )__name__
__module____qualname____doc__r   r   r
   r   intr   strr   rF   r   r   r   r   r      s        PP"o " " " Y ^" D Ds D4Z3H D D D YD D Dr   r   )	metaclassc                   H    e Zd ZdZdedddee         ddfdZd	efd
Z	d Z
dS )r?   z0Thread that feeds a TF queue from a InputReader.input_readerrC   ztf1.FIFOQueuer:   r-   ztf.dtypes.DTypec                 N   t           j                            |            t                                          | _        d| _        || _        || _        || _	        d |D             | _
        |                    t          t          || j
                                      | _        d S )NTc                 B    g | ]}t                               |          S r   )r;   placeholder)r!   r   s     r   r$   z)_QueueRunner.__init__.<locals>.<listcomp>x   s$    HHHS__U33HHHr   )	threadingThread__init__r;   get_default_sessionsessdaemonrO   r:   rC   placeholdersr@   dictzip
enqueue_op)r   rO   rC   r:   r-   s        r   rU   z_QueueRunner.__init__k   s     	!!$'''++--	(	
HHHHH--St7H-I-I(J(JKKr   r#   c                       fdt           j                  D             } j                             j        |           d S )Nc                 >    i | ]\  }}j         |         |         S r   )rY   )r!   ikeyr#   r   s      r   r)   z(_QueueRunner.enqueue.<locals>.<dictcomp>|   s+    TTTVQ!!$eCjTTTr   )	feed_dict)	enumerater:   rW   runr\   )r   r#   datas   `` r   r@   z_QueueRunner.enqueue{   sJ    TTTTTy?S?STTT	do66666r   c                     	 	 | j                                         }|                     |           n*# t          $ r t                              d           Y nw xY w[)NTzError reading from input)rO   r   r@   	Exceptionr6   	exception)r   r#   s     r   rc   z_QueueRunner.run   sp    	==)..00U#### = = =  !;<<<<<=		=s   .2 $AAN)rG   rH   rI   rJ   r   r   rL   rU   r
   r@   rc   r   r   r   r?   r?   h   s        ::L!L L 3i	L
 "L L L L 7_ 7 7 7 7= = = = =r   r?   )loggingrS   abcr   r   typingr   r   numpyr   ray.rllib.policy.sample_batchr   ray.rllib.utils.annotationsr   ray.rllib.utils.frameworkr	   ray.rllib.utils.typingr
   r   r;   r0   tfv	getLoggerrG   r6   r   rT   r?   r   r   r   <module>rr      sC        ' ' ' ' ' ' ' '             9 9 9 9 9 9 1 1 1 1 1 1 3 3 3 3 3 3 > > > > > > > >}R		8	$	$ R R R R RG R R R Rj= = = = =9# = = = = =r   