BVh$dZddlmZmZmZmZmZmZddlm Z ddl m Z ddl m Z ddl mZddl mZddlmZdd lmZdd lmZdd lmZ dd edefdedefdeeedeej0dedef dZ ddededeej4effdeeej4deej0dedeej4f dZy )z.condition_wrapper`saf f ctt|}dk(rg} rZtjj}| t d j | j Dcgc]}|}}ng} ||z}t|ttfs|f}|Dcgc]3}t|tjr|ntj|5}}|Dcgc]}t|tjs|!}}|Dcgc]}t|tjs| }}|||zk7r t d|Dcgc]}|j} }| k7rtdj| ||z }|stj d}|rt#j||}t$j&j)r\tjj} | d} t%j&} | j+tj,|d| }|Scc}wcc}wcc}wcc}wcc}w)zBWrapper around `body` that handles infeed queues and control deps.rNzCan't build training loop with infeed when there is no tpu_shard_context. Are you building a loop or graph directly rather than from inside tpu.rewrite, tpu.batch_parallel, tpu.shard, or tpu.replicate?zbTPU training loop body must return zero or more Tensor values followed by zero or more Operations.zNMismatch between input types and output types for training loop body: {} vs {})control_inputs)listrget_tpu_contextnumber_of_shards ValueErrorset_number_of_shardsgenerate_dequeue_op isinstancetupler Operationconvert_to_tensordtype TypeErrorformatr constantr r TensorTracer is_enabled trace_tpuget_default_graph)rr$d dequeue_opsoutputsooutput_operationsoutput_tensorsop output_types num_replicasttrrr input_typess r body_wrapperz while_loop..body_wrapperfsU &\Faf%557HH  !LM M''(89 , @ @ BC1QCkCkVk)+G ge} - g 3== )s/D/DQ/GGG %,Lqz!S]]/KLL!(;A'3==9;N;.#444  1 22(66BHH6L6l"  !6+|< >> $  ))!,n(--n=NPn!!,,.!113DDl    % % 'b||C$9$9$;$2D$02n kD M;7s$$ H!8H&H+5H+?#H0;H5rr!)rparallel_iterations) r r+r,lenr check_function_argument_countr-rnumber_of_tuple_elementsr r/ while_loop_tfr ) rrrrrxbody_arg_erroricondition_arg_errorrr?rr>s `` ` @@rr r s< 2+1&3&''*&;&;A&>&3&"()Q)+F +44 K'.  ##.-y&9QQ!&&9Q8RRlm{l| ~   ##.-y&9QQ!&&9Q8RRWXdX}X}W~//=.> @ AA99d$$  --8MTZC[qAFFC[B\]01 3 44  --8MTZC[qAFFC[B\]011h j   FTA  # $F  ! !vBA OOg&3):R :RD\ D\s"F F;F'F;F'F#ncdfd}fd}|dgn dg|z}t|||||}|}t|dk(r|djS|ddS)aBuilds a training loop that executes a fixed number of iterations. The set of loop-carried tensors correspond to `inputs`. `body` must be a function that takes and returns the values of the loop-carried tensors. Args: n: the number of loop iterations body: a Python function that builds the loop body. inputs: a list of initial values passed into the training loop or None (equivalent to an empty list). infeed_queue: if not None, the infeed queue from which to append a tuple of arguments as inputs to condition. name: (Deprecated) Does nothing. Returns: The final values of the loop-carried tensors. Raises: ValueError: if there is a type error. cJt|ttfs|gSt|SN)r(r"r))xss r_convert_to_listz repeat.._convert_to_lists b4- (Tk "Xorc~|kSrMr)rHargsrJs rcondzrepeat..conds q5Lrc(|dzg|zS)Nr!r)rHrQrOrs rr?zrepeat..body_wrappers E7%dDk2 22rNr)rrrr!)r rBr:) rJrrrrrRr?r6rOs `` @rrepeatrTst4 3.A3qc,r`s. CAA.++2=/*.6 .2>B TO(38,TOc3h'TOS *TO&h&:&:;TO TO%( TOt5937 / / 3j33X=>> ?/ T*//0 1/8//0 /  / j334 /r