&Vji ddlZddlmZmZmZddlZddlmZmZe edfZ eee e fge edffZ gdZdedefdZd ejjdeeeffd Zd eedeeeffd Zd edefdZdS)N)AnyCallableUnion)tree_flatten_with_pathtree_map.)normalize_source_namemodule_to_nested_dicttrack_dynamism_across_examplesclone_and_convert_to_metanamereturnc.tjdd|S)Nz\.([a-zA-Z_][a-zA-Z0-9_]*)z['\1'])resub)r s Y/root/voice-cloning/.venv/lib/python3.11/site-packages/torch/fx/experimental/_dynamism.pyrrs 6/D A AAmoduleci}i|d<i|d<t|D]} |dstt||sqt||}t |t jjsBt |ttt j frt|tur|||<#t$rYwxYw|dD]\}}||d|<|dD]\}}||d|<|D]\}}t#||d|<|S)ziRecursively converts an nn.Module into a nested dictionary with explicit 'parameters' and 'modules' keys. _parameters_modules_F)recurse)dir startswithcallablegetattr isinstancetorchnnModuleintfloatTensortypeboolNotImplementedErrornamed_parameters named_buffersnamed_childrenr )r self_dict attr_name attr_valuer parambuffer submodules rr r s "I!ImIj[[  '',, 6X **66 6%VY77 ":ux??6":UEL/IJJ6Z((44+5Ii("    H  ..u.==// e). - &&,,U,;;00 f)/ - &&!0022GGi&;I&F&F *d## sB#C CCexample_inputscBi}|D]}d|vr=t|dtjjrt |d|d<t |\}}|D]C\}}t|t ttjfs.t|tjrt|j }d}n|f}d}||vr,dtt|D|f||<ns||\} } | |kr t| t|krA| tt| t|kAt|D],\} } ||d| | -Ei} |D]d\}\} }td| D}ddd |Dz}|dj}|| vri| |<|| ||<e| S) a This function analyzes a list of example inputs to determine the dynamism of their shapes. It tracks whether the dimensions of tensors or non-tensor values change across different examples. The function returns a dictionary where each key represents a path to a value in the input examples, and the corresponding value is a tuple indicating which dimensions are dynamic (i.e., change across examples). This helps in understanding how the structure of data varies across different instances. selfTFc*g|]}tS)set).0rs r z2track_dynamism_across_examples..Vs&H&H&Hsuu&H&H&Hrrc3<K|]}t|dkVdS)N)len)r6ss r z1track_dynamism_across_examples..bs,77#a&&1*777777rLc36K|]}t|VdS)N)str)r6ks rr<z1track_dynamism_across_examples..cs*>>3q66 >>>>>>r)rrrr r rr!r"r#tupleshaperanger:appendr5 enumerateadditemsjoinkey)r0trackingexleaves_with_pathsrkey_pathvaluerC is_tensordim_setsflagidimoutput _is_tensor final_dynkey_strrJs rr r ;sL<>H22 R<>X>>>>>>qko f  F3K(s G Mr example_inputcFdtdtfd}t||S)z This function takes a list of example inputs and for each tensor, clones it and converts it to device=meta. For non-tensor values, it keeps the reference. It uses pytree to handle nested structures recursively. rOr ct|tjr(|dS|S)Nmeta)device)rrr#cloneto)rOs r transform_fnz/clone_and_convert_to_meta..transform_fnqs: eU\ * * 3;;==##6#22 2 r)rr)rYr`s rr r ks6 CC L- 0 00r)rtypingrrrrtorch.utils._pytreerrrBKeyPathr!r"NonTensorShapeFn__all__r@rrr dictr listr r r4rrrhsE '''''''''' @@@@@@@@ S/U3:./sCx@A   BBBBBB %(/ d38n    F-I- #s(^----` 1S 1S 1 1 1 1 1 1r