Add python script to convert QE json model to binary and vice versa

quality/lr/README.md

@@ -0,0 +1,27 @@
+# Quality Model Tool
+
+- The python script ```qualityestimator_json_to_bin.py``` converts a logistic regressor quality estimator model from json to binary file and vice versa.
+
+- To converts a json to binary:
+
+```console
+  python qualityestimator_json_to_bin.py --to_json qe_model.json --out qe_model.bin
+```
+
+- To converts a binary to json:
+
+```console
+  python qualityestimator_json_to_bin.py --from_json qe_model.bin --out qe_model.json
+```
+
+- The json must follow this structure:
+```json
+{
+    "mean_": [ 0.0, 0.0, 0.0, 0.0, ], 
+    "scale_": [ 0.0, 0.0, 0.0, 0.0, ],
+    "coef_": [ 0.0, 0.0, 0.0, 0.0, ], 
+    "intercept_": 0.0 
+}
+```
+
+- The binary file will have the following structure defined on [LogisticRegressorQualityEstimator](https://github.com/browsermt/bergamot-translator/blob/main/src/translator/quality_estimator.h#L100-L108).

quality/lr/qualityestimator_json_to_bin.py

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+import argparse
+import json
+import struct
+from collections import namedtuple
+
+# magic(uint64_t), lrParametersDims(uint64_t)
+Header_fmt = "<1Q1Q"
+Header_len = struct.calcsize(Header_fmt)
+
+QE_MAGIC_NUMBER = 8704388732126802304
+
+
+def from_qe_file(file):
+    magic, paramDim = struct.unpack(Header_fmt, file.read(Header_len))
+
+    if magic != QE_MAGIC_NUMBER:
+        print("Invalid quality estimator file.")
+        exit(1)
+
+    # scale_[N] + mean_[N] + coef_[N] + intercept_
+    lrParams_fmt = f"<{3*paramDim+1}f"
+
+    lrParams_size = struct.calcsize(lrParams_fmt)
+
+    params = list(struct.unpack(lrParams_fmt, file.read(lrParams_size)))
+
+    lrParams = {}
+    lrParams["scale_"] = params[:paramDim]
+    lrParams["mean_"] = params[paramDim : 2 * paramDim]
+    lrParams["coef_"] = params[2 * paramDim : 3 * paramDim]
+    lrParams["intercept_"] = params[3 * paramDim]
+
+    return lrParams
+
+
+def to_binary(lrParams):
+
+    paramDims = len(lrParams["scale_"])
+
+    if paramDims != len(lrParams["mean_"]) and paramDims != len(
+        lrParams["coef_"]
+    ):
+        print("Invalid LR parameters.")
+        exit(1)
+
+    lrParams_fmt = f"<{3*paramDims+1}f"
+
+    params = (
+        lrParams["scale_"]
+        + lrParams["mean_"]
+        + lrParams["coef_"]
+        + [lrParams["intercept_"]]
+    )
+
+    return struct.pack(Header_fmt, QE_MAGIC_NUMBER, paramDims) + struct.pack(
+        lrParams_fmt, *params
+    )
+
+
+parser = argparse.ArgumentParser(description="Read and write quality estimator files.")
+parser.add_argument(
+    "--to_json", type=argparse.FileType("rb"), help="Read quality estimator file"
+)
+parser.add_argument(
+    "--from_json",
+    type=argparse.FileType("r"),
+    help="Read json file and generate quality estimator binary",
+)
+parser.add_argument(
+    "--out",
+    type=argparse.FileType("wb"),
+    help="Output generated data from to_json or from_json option",
+)
+
+args = parser.parse_args()
+
+output = None
+
+if args.to_json:
+    output = json.dumps(from_qe_file(args.to_json), indent=3)
+elif args.from_json:
+    output = to_binary(json.loads(args.from_json.read()))
+
+if output is None:
+    exit(0)
+
+if args.out:
+    args.out.write(output.encode("UTF-8") if type(output) is str else output)
+    args.out.close()
+else:
+    print(output)