# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for analyzer package."""

import io
import sys
import tempfile

import tensorflow as tf

from tensorflow.lite.python import analyzer
from tensorflow.lite.python import lite
from tensorflow.python.framework import test_util
from tensorflow.python.platform import resource_loader
from tensorflow.python.platform import test
from tensorflow.python.trackable import autotrackable


class AnalyzerTest(test_util.TensorFlowTestCase):

  def testTxt(self):
    model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(model_path=model_path)
    txt = mock_stdout.getvalue()
    self.assertIn('Subgraph#0(T#1) -> [T#2]', txt)
    self.assertIn('Op#0 ADD(T#1, T#1) -> [T#0]', txt)
    self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)
    self.assertNotIn('Your model looks compatible with GPU delegate', txt)

  def testMlir(self):
    model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(
          model_path=model_path, experimental_use_mlir=True)
    mlir = mock_stdout.getvalue()
    self.assertIn(
        'func @main(%arg0: tensor<1x8x8x3xf32> '
        '{tf_saved_model.index_path = ["a"]}) -> '
        '(tensor<1x8x8x3xf32> {tf_saved_model.index_path = ["x"]}) attributes '
        '{tf.entry_function = {inputs = "input", outputs = "output"}, '
        'tf_saved_model.exported_names = ["serving_default"]}', mlir)
    self.assertIn(
        '%0 = tfl.add %arg0, %arg0 {fused_activation_function = "NONE"} : '
        'tensor<1x8x8x3xf32>', mlir)
    self.assertIn(
        '%1 = tfl.add %0, %arg0 {fused_activation_function = "NONE"} : '
        'tensor<1x8x8x3xf32>', mlir)
    self.assertIn('return %1 : tensor<1x8x8x3xf32>', mlir)

  def testMlirHugeConst(self):
    model_path = resource_loader.get_path_to_datafile(
        '../testdata/conv_huge_im2col.bin')
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(
          model_path=model_path, experimental_use_mlir=True)
    mlir = mock_stdout.getvalue()
    self.assertIn(
        '%1 = "tfl.pseudo_const"() <{value = dense_resource<__elided__> : '
        'tensor<3x3x3x8xf32>}> : () -> tensor<3x3x3x8xf32>', mlir)

  def testTxtWithFlatBufferModel(self):

    @tf.function(
        input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
    def func(x):
      return x + tf.cos(x)

    converter = lite.TFLiteConverterV2.from_concrete_functions(
        [func.get_concrete_function()], func)
    fb_model = converter.convert()
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
    txt = mock_stdout.getvalue()
    self.assertIn('Subgraph#0 main(T#0) -> [T#2]', txt)
    self.assertIn('Op#0 COS(T#0) -> [T#1]', txt)
    self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)

  def testMlirWithFlatBufferModel(self):

    @tf.function(
        input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
    def func(x):
      return x + tf.cos(x)

    converter = lite.TFLiteConverterV2.from_concrete_functions(
        [func.get_concrete_function()], func)
    fb_model = converter.convert()
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(
          model_content=fb_model, experimental_use_mlir=True)
    mlir = mock_stdout.getvalue()
    self.assertIn('func @main(%arg0: tensor<?xf32>) -> tensor<?xf32>', mlir)
    self.assertIn('%0 = "tfl.cos"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>',
                  mlir)
    self.assertIn(
        '%1 = tfl.add %arg0, %0 {fused_activation_function = "NONE"} : '
        'tensor<?xf32>', mlir)
    self.assertIn('return %1 : tensor<?xf32', mlir)

  def testTxtSignatureDefs(self):
    with tempfile.TemporaryDirectory() as tmp_dir:

      @tf.function(input_signature=[
          tf.TensorSpec(shape=None, dtype=tf.float32),
          tf.TensorSpec(shape=None, dtype=tf.float32)
      ])
      def add(a, b):
        return {'add_result': tf.add(a, b)}

      @tf.function(input_signature=[
          tf.TensorSpec(shape=None, dtype=tf.float32),
          tf.TensorSpec(shape=None, dtype=tf.float32)
      ])
      def sub(x, y):
        return {'sub_result': tf.subtract(x, y)}

      root = autotrackable.AutoTrackable()
      root.f1 = add.get_concrete_function()
      root.f2 = sub.get_concrete_function()

      tf.saved_model.save(
          root, tmp_dir, signatures={
              'add': root.f1,
              'sub': root.f2
          })

      converter = lite.TFLiteConverterV2.from_saved_model(tmp_dir)
      fb_model = converter.convert()
      mock_stdout = io.StringIO()
      with test.mock.patch.object(sys, 'stdout', mock_stdout):
        analyzer.ModelAnalyzer.analyze(model_content=fb_model)
      txt = mock_stdout.getvalue()
      self.assertIn("Your TFLite model has '2' signature_def(s).", txt)
      self.assertIn("Signature#0 key: 'add'", txt)
      self.assertIn("  'a' : T#1", txt)
      self.assertIn("  'b' : T#0", txt)
      self.assertIn("  'add_result' : T#2", txt)
      self.assertIn("Signature#1 key: 'sub'", txt)
      self.assertIn("  'x' : T#1_1", txt)
      self.assertIn("  'y' : T#1_0", txt)
      self.assertIn("  'sub_result' : T#1_2", txt)

  def testTxtWithoutInput(self):

    @tf.function()
    def func():
      return tf.cos(1.0)

    converter = lite.TFLiteConverterV2.from_concrete_functions(
        [func.get_concrete_function()], func)
    fb_model = converter.convert()
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
    txt = mock_stdout.getvalue()
    self.assertIn('Subgraph#0 main() -> [T#0]', txt)

  def testTxtWithEinsum(self):

    @tf.function(input_signature=[
        tf.TensorSpec(shape=[1, 100, 512], dtype=tf.float32),
        tf.TensorSpec(shape=[512, 8, 64], dtype=tf.float32)
    ])
    def func(lhs, rhs):
      return tf.einsum('ABD,DNH->ABNH', lhs, rhs)

    converter = lite.TFLiteConverterV2.from_concrete_functions(
        [func.get_concrete_function()], func)
    converter.unfold_batchmatmul = True
    fb_model = converter.convert()
    mock_stdout = io.StringIO()
    with test.mock.patch.object(sys, 'stdout', mock_stdout):
      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
    txt = mock_stdout.getvalue()
    self.assertIn('Op#0 RESHAPE(T#1, T#4[512, 512]) -> [T#5]', txt)
    self.assertIn('Op#1 TRANSPOSE(T#5, T#3[1, 0]) -> [T#6]', txt)
    self.assertIn('Op#2 FULLY_CONNECTED(T#0, T#6, T#-1) -> [T#7]', txt)
    self.assertIn('Op#3 RESHAPE(T#7, T#2[1, 100, 8, 64]) -> [T#8]', txt)
    self.assertIn(
        'T#2(einsum/Einsum) shape:[4], type:INT32 RO 16 bytes, '
        'buffer: 3, data:[1, 100, 8, 64]', txt)
    self.assertIn(
        'T#3(einsum/Einsum2) shape:[2], type:INT32 RO 8 bytes, '
        'buffer: 4, data:[1, 0]', txt)
    self.assertIn(
        'T#4(einsum/Einsum3) shape:[2], type:INT32 RO 8 bytes, '
        'buffer: 5, data:[512, 512]', txt)


if __name__ == '__main__':
  test.main()