o ®Ÿ?e§ã@s®dZddlmZddlmZedƒ ddd„ƒZed ƒ  dd d „ƒZed ƒ dd d„ƒZedƒ  ddd„ƒZedƒddd„ƒZ edƒdd„ƒZ edƒ   d dd„ƒZ dS)!zDmlir is an experimental library that provides support APIs for MLIR.é)Ú pywrap_mlir)Ú tf_exportz#mlir.experimental.convert_graph_defútf-standard-pipelineFcCót |||¡S)aKImport a GraphDef and convert it to a textual MLIR module. This API is only intended for inspecting the internals of TensorFlow and the string returned is at the moment intended for debugging purposes. Args: graph_def: An object of type graph_pb2.GraphDef or a textual proto representation of a valid GraphDef. pass_pipeline: A textual description of an MLIR Pass Pipeline to run on the module, see MLIR documentation for the [textual pass pipeline syntax](https://mlir.llvm.org/docs/PassManagement/#textual-pass-pipeline-specification). show_debug_info: Whether to include locations in the emitted textual form. Returns: A textual representation of the MLIR module corresponding to the graphdef. Raises: InvalidArgumentError: if graph_def is invalid or cannot be converted to MLIR. )rZimport_graphdef)Z graph_defÚ pass_pipelineÚshow_debug_info©rúe/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/compiler/mlir/mlir.pyÚconvert_graph_defsr z"mlir.experimental.convert_functioncCr)a¨Import a ConcreteFunction and convert it to a textual MLIR module. This API is only intended for inspecting the internals of TensorFlow and the string returned is at the moment intended for debugging purposes. A [tf.function](https://www.tensorflow.org/api_docs/python/tf/function) can be imported and converted from TensorFlow to TensorFlow MLIR with this API by extracting its ConcreteFunction (eagerly-executing wrapper around a [tf.Graph](https://www.tensorflow.org/api_docs/python/tf/Graph)). For example: >>> @tf.function ... def add(a, b): ... return a + b >>> concrete_function = add.get_concrete_function( ... tf.TensorSpec(None, tf.dtypes.float32), ... tf.TensorSpec(None, tf.dtypes.float32)) >>> tf.mlir.experimental.convert_function(concrete_function) '...module attributes {...} {...}...' Args: concrete_function: An object of type ConcreteFunction. pass_pipeline: A textual description of an MLIR Pass Pipeline to run on the module, see MLIR documentation for the [textual pass pipeline syntax](https://mlir.llvm.org/docs/PassManagement/#textual-pass-pipeline-specification). show_debug_info: Whether to include locations in the emitted textual form. Returns: A textual representation of the MLIR module corresponding to the ConcreteFunction. 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Returns: A textual representation of the MLIR module corresponding to the transformed module. )rZexperimental_run_pass_pipeline)Úmlir_txtrrrrr Úrun_pass_pipeline˜s ÿrz mlir.experimental.write_bytecodecCst ||¡dS)z‹Writes an MLIR module out as bytecode. Args: filename: The filename to write to. mlir_txt: The MLIR module in textual format. N)rÚexperimental_write_bytecode)Úfilenamerrrr rªsrz)mlir.experimental.tflite_to_tosa_bytecodeNcCst |||||¡dS)a©Converts TFLite flatbuffer to TOSA dialect in MLIR bytecode. Args: flatbuffer: Path to flatbuffer. bytecode: Path to output bytecode. use_external_constant: Whether to create `tfl.external_const` instead of `tfl.const`. ordered_input_arrays: ordered_output_arrays: If ordered_output_arrays is not empty, then the function will only return nodes in ordered_output_arrays in the same order N)rZ$experimental_tflite_to_tosa_bytecode)Z flatbufferÚbytecodeZuse_external_constantZordered_input_arraysZordered_output_arraysrrr Útflite_to_tosa_bytecodeµsûr)rF)F)TF)FNN) Ú__doc__Ztensorflow.pythonrZ tensorflow.python.util.tf_exportrr r rrrrrrrrr Ús4   ÿ ý. ÿ ù$   û