import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu

            

Reported by Pylint.

              
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu


            

Reported by Pylint.

              import caffe2.python.mkl_test_util as mu


@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLConvTest(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),

            

Reported by Pylint.

                  @settings(max_examples=2, deadline=100)
    def test_mkl_convolution(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, gc, dc):
        op = core.CreateOperator(
            "Conv",
            ["X", "w", "b"],
            ["Y"],
            stride=stride,

            

Reported by Pylint.

              

if __name__ == "__main__":
    import unittest
    unittest.main()

            

Reported by Pylint.

              




import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np

            

Reported by Pylint.

              
@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLConvTest(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(8, 8),
           input_channels=st.integers(1, 3),

            

Reported by Pylint.

                         batch_size=st.integers(1, 3),
           **mu.gcs)
    @settings(max_examples=2, deadline=100)
    def test_mkl_convolution(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, gc, dc):
        op = core.CreateOperator(
            "Conv",
            ["X", "w", "b"],

            

Reported by Pylint.

                         batch_size=st.integers(1, 3),
           **mu.gcs)
    @settings(max_examples=2, deadline=100)
    def test_mkl_convolution(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, gc, dc):
        op = core.CreateOperator(
            "Conv",
            ["X", "w", "b"],

            

Reported by Pylint.

                         batch_size=st.integers(1, 3),
           **mu.gcs)
    @settings(max_examples=2, deadline=100)
    def test_mkl_convolution(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, gc, dc):
        op = core.CreateOperator(
            "Conv",
            ["X", "w", "b"],

            

Reported by Pylint.

              

import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu

            

Reported by Pylint.

              
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu


            

Reported by Pylint.

              import caffe2.python.mkl_test_util as mu


@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")


class MKLLRNTest(hu.HypothesisTestCase):
    @given(input_channels=st.integers(1, 3),

            

Reported by Pylint.

              
    def test_mkl_LRN(self, input_channels,
                            batch_size, im_size, order,
                             gc, dc):
        op = core.CreateOperator(
            "LRN",
            ["X"],
            ["Y", "Y_scale"],
            size=5,

            

Reported by Pylint.

              

if __name__ == "__main__":
    import unittest
    unittest.main()

            

Reported by Pylint.

              




import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np

            

Reported by Pylint.

              




import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np

            

Reported by Pylint.

                               "Skipping as we do not have mkldnn.")


class MKLLRNTest(hu.HypothesisTestCase):
    @given(input_channels=st.integers(1, 3),
           batch_size=st.integers(1, 3),
           im_size=st.integers(1, 10),
           order=st.sampled_from(["NCHW"]),
           **mu.gcs)

            

Reported by Pylint.

                         im_size=st.integers(1, 10),
           order=st.sampled_from(["NCHW"]),
           **mu.gcs)

    def test_mkl_LRN(self, input_channels,
                            batch_size, im_size, order,
                             gc, dc):
        op = core.CreateOperator(
            "LRN",

            

Reported by Pylint.

                         im_size=st.integers(1, 10),
           order=st.sampled_from(["NCHW"]),
           **mu.gcs)

    def test_mkl_LRN(self, input_channels,
                            batch_size, im_size, order,
                             gc, dc):
        op = core.CreateOperator(
            "LRN",

            

Reported by Pylint.

                  return parser


def benchmark(args):
    print('Batch size: {}'.format(args.batch_size))
    mf = ModelDownloader()
    init_net, pred_net, value_info = mf.get_c2_model(args.model)
    input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()}
    print("input info: {}".format(input_shapes))

            

Reported by Pylint.

                          #print("{}".format(name))
            workspace.FeedBlob(name, blob, device_option)
        workspace.CreateNet(pred_net)
        start = time.time()
        res = workspace.BenchmarkNet(pred_net.name,
                                     args.warmup_iterations,
                                     args.iterations,
                                     args.layer_wise_benchmark)
        print("FPS: {:.2f}".format(1/res[0]*1000*args.batch_size))

            

Reported by Pylint.

              




from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.models.download import ModelDownloader
import numpy as np

            

Reported by Pylint.

              from caffe2.python import core, workspace
from caffe2.python.models.download import ModelDownloader
import numpy as np
import argparse
import time


def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")

            

Reported by Pylint.

              from caffe2.python.models.download import ModelDownloader
import numpy as np
import argparse
import time


def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
    parser.add_argument(

            

Reported by Pylint.

              import time


def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=128,

            

Reported by Pylint.

              import time


def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=128,

            

Reported by Pylint.

                  return parser


def benchmark(args):
    print('Batch size: {}'.format(args.batch_size))
    mf = ModelDownloader()
    init_net, pred_net, value_info = mf.get_c2_model(args.model)
    input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()}
    print("input info: {}".format(input_shapes))

            

Reported by Pylint.

                  return parser


def benchmark(args):
    print('Batch size: {}'.format(args.batch_size))
    mf = ModelDownloader()
    init_net, pred_net, value_info = mf.get_c2_model(args.model)
    input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()}
    print("input info: {}".format(input_shapes))

            

Reported by Pylint.

              
def benchmark(args):
    print('Batch size: {}'.format(args.batch_size))
    mf = ModelDownloader()
    init_net, pred_net, value_info = mf.get_c2_model(args.model)
    input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()}
    print("input info: {}".format(input_shapes))
    external_inputs = {}
    for k, v in input_shapes.items():

            

Reported by Pylint.

              




import sys
import numpy as np

def print_test_debug_info(testname, items_dict):

            

Reported by Pylint.

              import sys
import numpy as np

def print_test_debug_info(testname, items_dict):
    filename = "debug_operator_onnxifi_" + testname + ".txt"
    np.set_printoptions(threshold=sys.maxsize)
    with open(filename, 'w') as f:
        for key, value in items_dict.items():
            print(key, value)

            

Reported by Pylint.

              def print_test_debug_info(testname, items_dict):
    filename = "debug_operator_onnxifi_" + testname + ".txt"
    np.set_printoptions(threshold=sys.maxsize)
    with open(filename, 'w') as f:
        for key, value in items_dict.items():
            print(key, value)
            f.write("{}\n".format(key))
            f.write("{}\n".format(value))


            

Reported by Pylint.

                          f.write("{}\n".format(key))
            f.write("{}\n".format(value))

def print_net(net):
    for i in net.external_input:
        print("Input: {}".format(i))
    for i in net.external_output:
        print("Output: {}".format(i))
    for op in net.op:

            

Reported by Pylint.

                      print("Input: {}".format(i))
    for i in net.external_output:
        print("Output: {}".format(i))
    for op in net.op:
        print("Op {}".format(op.type))
        for x in op.input:
            print("  input: {}".format(x))
        for y in op.output:
            print("  output: {}".format(y))

            

Reported by Pylint.

                      print("Output: {}".format(i))
    for op in net.op:
        print("Op {}".format(op.type))
        for x in op.input:
            print("  input: {}".format(x))
        for y in op.output:
            print("  output: {}".format(y))

def _sigmoid(x):

            

Reported by Pylint.

                      print("Op {}".format(op.type))
        for x in op.input:
            print("  input: {}".format(x))
        for y in op.output:
            print("  output: {}".format(y))

def _sigmoid(x):
    return 1. / (1. + np.exp(np.float64(-x)))


            

Reported by Pylint.

                      for y in op.output:
            print("  output: {}".format(y))

def _sigmoid(x):
    return 1. / (1. + np.exp(np.float64(-x)))

def _tanh(x):
    return np.tanh(np.float64(x))


            

Reported by Pylint.

              def _sigmoid(x):
    return 1. / (1. + np.exp(np.float64(-x)))

def _tanh(x):
    return np.tanh(np.float64(x))

def _swish(x):
    return np.float64(x) * _sigmoid(x)


            

Reported by Pylint.

              def _tanh(x):
    return np.tanh(np.float64(x))

def _swish(x):
    return np.float64(x) * _sigmoid(x)

def _gelu_by_sigmoid(x):
    return np.float64(x) / (1. + np.exp(np.float64(x) * 1.702))


            

Reported by Pylint.

              

import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu

            

Reported by Pylint.

              
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu


            

Reported by Pylint.

              import caffe2.python.mkl_test_util as mu


@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):

            

Reported by Pylint.

              class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):
        X = np.random.rand(m, n).astype(np.float32)
        op = core.CreateOperator(
            "Sigmoid",
            ["X"],
            ["Y" if not inplace else "X"]

            

Reported by Pylint.

              

if __name__ == "__main__":
    import unittest
    unittest.main()

            

Reported by Pylint.

              




import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np

            

Reported by Pylint.

              
@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):
        X = np.random.rand(m, n).astype(np.float32)
        op = core.CreateOperator(

            

Reported by Pylint.

              class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):
        X = np.random.rand(m, n).astype(np.float32)
        op = core.CreateOperator(
            "Sigmoid",
            ["X"],
            ["Y" if not inplace else "X"]

            

Reported by Pylint.

              class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):
        X = np.random.rand(m, n).astype(np.float32)
        op = core.CreateOperator(
            "Sigmoid",
            ["X"],
            ["Y" if not inplace else "X"]

            

Reported by Pylint.

              class MKLSigmoidTest(hu.HypothesisTestCase):
    @given(n=st.integers(1, 5), m=st.integers(1, 5), inplace=st.booleans(),
           **mu.gcs)
    def test_mkl_sigmoid(self, n, m, inplace, gc, dc):
        X = np.random.rand(m, n).astype(np.float32)
        op = core.CreateOperator(
            "Sigmoid",
            ["X"],
            ["Y" if not inplace else "X"]

            

Reported by Pylint.

              




from caffe2.python import net_printer
from caffe2.python.checkpoint import Job
from caffe2.python.net_builder import ops
from caffe2.python.task import Task, final_output, WorkspaceType

            

Reported by Pylint.

              from caffe2.python.checkpoint import Job
from caffe2.python.net_builder import ops
from caffe2.python.task import Task, final_output, WorkspaceType
import unittest


def example_loop():
    with Task():
        total = ops.Const(0)

            

Reported by Pylint.

              import unittest


def example_loop():
    with Task():
        total = ops.Const(0)
        total_large = ops.Const(0)
        total_small = ops.Const(0)
        total_tiny = ops.Const(0)

            

Reported by Pylint.

                          outer = ops.Mul([loop.iter(), ops.Const(10)])
            with ops.loop(loop.iter()) as inner:
                val = ops.Add([outer, inner.iter()])
                with ops.If(ops.GE([val, ops.Const(80)])) as c:
                    ops.Add([total_large, val], [total_large])
                with c.Elif(ops.GE([val, ops.Const(50)])) as c:
                    ops.Add([total_small, val], [total_small])
                with c.Else():
                    ops.Add([total_tiny, val], [total_tiny])

            

Reported by Pylint.

                              val = ops.Add([outer, inner.iter()])
                with ops.If(ops.GE([val, ops.Const(80)])) as c:
                    ops.Add([total_large, val], [total_large])
                with c.Elif(ops.GE([val, ops.Const(50)])) as c:
                    ops.Add([total_small, val], [total_small])
                with c.Else():
                    ops.Add([total_tiny, val], [total_tiny])
                ops.Add([total, val], total)


            

Reported by Pylint.

                              ops.Add([total, val], total)


def example_task():
    with Task():
        with ops.task_init():
            one = ops.Const(1)
        two = ops.Add([one, one])
        with ops.task_init():

            

Reported by Pylint.

                      six = ops.Add([accum, one])
        ops.Add([accum, one], [accum])
        seven_2 = ops.Add([accum, one])
        o6 = final_output(six)
        o7_1 = final_output(seven_1)
        o7_2 = final_output(seven_2)

    with Task(num_instances=2):
        with ops.task_init():

            

Reported by Pylint.

              
    return o6, o7_1, o7_2

def example_job():
    with Job() as job:
        with job.init_group:
            example_loop()
        example_task()
    return job

            

Reported by Pylint.

                  return job


class TestNetPrinter(unittest.TestCase):
    def test_print(self):
        self.assertTrue(len(net_printer.to_string(example_job())) > 0)

    def test_valid_job(self):
        job = example_job()

            

Reported by Pylint.

              

class TestNetPrinter(unittest.TestCase):
    def test_print(self):
        self.assertTrue(len(net_printer.to_string(example_job())) > 0)

    def test_valid_job(self):
        job = example_job()
        with job:

            

Reported by Pylint.

              
  if (at::hasCUDA()) {
    auto r = next_h.to(at::Device(kCUDA), kFloat, /*non_blocking=*/ false, /*copy=*/ true);
    ASSERT_TRUE(r.to(at::Device(kCPU), kFloat, /*non_blocking=*/ false, /*copy=*/ true).equal(next_h));
  }
  // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
  ASSERT_NO_THROW(randn({10, 10, 2}, options));

  // check Scalar.toTensor on Scalars backed by different data types

            

Reported by FlawFinder.

              }

TEST(TestScalar, TestEqual) {
  ASSERT_FALSE(Scalar(1.0).equal(false));
  ASSERT_FALSE(Scalar(1.0).equal(true));
  ASSERT_FALSE(Scalar(true).equal(1.0));
  ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));

            

Reported by FlawFinder.

              
TEST(TestScalar, TestEqual) {
  ASSERT_FALSE(Scalar(1.0).equal(false));
  ASSERT_FALSE(Scalar(1.0).equal(true));
  ASSERT_FALSE(Scalar(true).equal(1.0));
  ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));

            

Reported by FlawFinder.

              TEST(TestScalar, TestEqual) {
  ASSERT_FALSE(Scalar(1.0).equal(false));
  ASSERT_FALSE(Scalar(1.0).equal(true));
  ASSERT_FALSE(Scalar(true).equal(1.0));
  ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

            

Reported by FlawFinder.

                ASSERT_FALSE(Scalar(1.0).equal(false));
  ASSERT_FALSE(Scalar(1.0).equal(true));
  ASSERT_FALSE(Scalar(true).equal(1.0));
  ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));


            

Reported by FlawFinder.

                ASSERT_FALSE(Scalar(true).equal(1.0));
  ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

  ASSERT_TRUE(Scalar(2.0).equal(c10::complex<double>{2.0, 0.0}));
  ASSERT_FALSE(Scalar(2.0).equal(c10::complex<double>{2.0, 4.0}));

            

Reported by FlawFinder.

                ASSERT_TRUE(Scalar(true).equal(true));

  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

  ASSERT_TRUE(Scalar(2.0).equal(c10::complex<double>{2.0, 0.0}));
  ASSERT_FALSE(Scalar(2.0).equal(c10::complex<double>{2.0, 4.0}));
  ASSERT_FALSE(Scalar(2.0).equal(3.0));

            

Reported by FlawFinder.

              
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 5.0}).equal(c10::complex<double>{2.0, 5.0}));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

  ASSERT_TRUE(Scalar(2.0).equal(c10::complex<double>{2.0, 0.0}));
  ASSERT_FALSE(Scalar(2.0).equal(c10::complex<double>{2.0, 4.0}));
  ASSERT_FALSE(Scalar(2.0).equal(3.0));
  ASSERT_TRUE(Scalar(2.0).equal(2));

            

Reported by FlawFinder.

                ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2.0));
  ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

  ASSERT_TRUE(Scalar(2.0).equal(c10::complex<double>{2.0, 0.0}));
  ASSERT_FALSE(Scalar(2.0).equal(c10::complex<double>{2.0, 4.0}));
  ASSERT_FALSE(Scalar(2.0).equal(3.0));
  ASSERT_TRUE(Scalar(2.0).equal(2));

  ASSERT_TRUE(Scalar(2).equal(c10::complex<double>{2.0, 0}));

            

Reported by FlawFinder.

                ASSERT_TRUE(Scalar(c10::complex<double>{2.0, 0}).equal(2));

  ASSERT_TRUE(Scalar(2.0).equal(c10::complex<double>{2.0, 0.0}));
  ASSERT_FALSE(Scalar(2.0).equal(c10::complex<double>{2.0, 4.0}));
  ASSERT_FALSE(Scalar(2.0).equal(3.0));
  ASSERT_TRUE(Scalar(2.0).equal(2));

  ASSERT_TRUE(Scalar(2).equal(c10::complex<double>{2.0, 0}));
  ASSERT_TRUE(Scalar(2).equal(2));

            

Reported by FlawFinder.

                          assert len(shape) >= axis,\
                "Concat expects that limited dimensions of the input tensor"
            shapes.append(shape)
        logger.info('Concat Layer input shapes: ' + str(shapes))

        if axis == 0:
            self.output_schema = schema.from_blob_list(
                input_record[0],
                [self.get_next_blob_reference('output')]

            

Reported by Pylint.

                      output_dims = shapes[0]
        output_dims[axis - 1] = concat_dim

        logger.info('Concat Layer output_dims: ' + str(output_dims))
        self.output_schema = schema.Scalar(
            (np.float32, output_dims),
            self.get_next_blob_reference('output'))

        record_to_concat = input_record.fields.values()

            

Reported by Pylint.

              ## @package concat
# Module caffe2.python.layers.concat





from caffe2.python import schema
from caffe2.python.layers.layers import (

            

Reported by Pylint.

              )
from future.utils import viewitems
import numpy as np
from collections import defaultdict

import logging
logger = logging.getLogger(__name__)



            

Reported by Pylint.

              import numpy as np
from collections import defaultdict

import logging
logger = logging.getLogger(__name__)


def get_concatenated_feature_to_index(blobs_to_concat):
    concat_feature_to_index = defaultdict(list)

            

Reported by Pylint.

              logger = logging.getLogger(__name__)


def get_concatenated_feature_to_index(blobs_to_concat):
    concat_feature_to_index = defaultdict(list)
    start_pos = 0
    for scalar in blobs_to_concat:
        num_dims = scalar.dtype.shape[0]
        if hasattr(scalar, 'metadata') \

            

Reported by Pylint.

                          and hasattr(scalar.metadata, 'feature_specs') \
            and hasattr(scalar.metadata.feature_specs, 'feature_to_index') \
                and isinstance(scalar.metadata.feature_specs.feature_to_index, dict):  # noqa B950
            for k, v in scalar.metadata.feature_specs.feature_to_index.items():
                concat_feature_to_index[k].extend([start_pos + vi for vi in v])
        start_pos += num_dims
    return dict(concat_feature_to_index) if concat_feature_to_index.keys() else None



            

Reported by Pylint.

                      )
    """

    def __init__(self, model, input_record, axis=1, add_axis=0,
                 name='concat', **kwargs):
        super(Concat, self).__init__(model, name, input_record, **kwargs)
        self.axis = axis
        self.add_axis = add_axis
        assert not (axis == 0 and add_axis == 1), \

            

Reported by Pylint.

                      )
    """

    def __init__(self, model, input_record, axis=1, add_axis=0,
                 name='concat', **kwargs):
        super(Concat, self).__init__(model, name, input_record, **kwargs)
        self.axis = axis
        self.add_axis = add_axis
        assert not (axis == 0 and add_axis == 1), \

            

Reported by Pylint.

              
    def __init__(self, model, input_record, axis=1, add_axis=0,
                 name='concat', **kwargs):
        super(Concat, self).__init__(model, name, input_record, **kwargs)
        self.axis = axis
        self.add_axis = add_axis
        assert not (axis == 0 and add_axis == 1), \
            "It's not allowed to add axis=0"
        assert isinstance(input_record, schema.Struct),\

            

Reported by Pylint.

              from dataclasses import dataclass, field
from typing import Optional, Dict


def X(val):
    """
    Compact way to write a leaf node
    """
    return val, []

            

Reported by Pylint.

              from typing import Optional, Dict


def X(val):
    """
    Compact way to write a leaf node
    """
    return val, []


            

Reported by Pylint.

                  return val, []


def XImportant(name):
    """Compact way to write an important (run on PRs) leaf node"""
    return (name, [("important", [X(True)])])


@dataclass

            

Reported by Pylint.

              

@dataclass
class ConfigNode:
    parent: Optional['ConfigNode']
    node_name: str
    props: Dict[str, str] = field(default_factory=dict)

    def get_label(self):

            

Reported by Pylint.

                  node_name: str
    props: Dict[str, str] = field(default_factory=dict)

    def get_label(self):
        return self.node_name

    # noinspection PyMethodMayBeStatic
    def get_children(self):
        return []

            

Reported by Pylint.

                      return self.node_name

    # noinspection PyMethodMayBeStatic
    def get_children(self):
        return []

    def get_parents(self):
        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []


            

Reported by Pylint.

                      return self.node_name

    # noinspection PyMethodMayBeStatic
    def get_children(self):
        return []

    def get_parents(self):
        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []


            

Reported by Pylint.

                  def get_children(self):
        return []

    def get_parents(self):
        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []

    def get_depth(self):
        return len(self.get_parents())


            

Reported by Pylint.

                  def get_parents(self):
        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []

    def get_depth(self):
        return len(self.get_parents())

    def get_node_key(self):
        return "%".join(self.get_parents() + [self.get_label()])


            

Reported by Pylint.

                  def get_depth(self):
        return len(self.get_parents())

    def get_node_key(self):
        return "%".join(self.get_parents() + [self.get_label()])

    def find_prop(self, propname, searched=None):
        """
        Checks if its own dictionary has

            

Reported by Pylint.

              

import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu

            

Reported by Pylint.

              import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):

            

Reported by Pylint.

              import caffe2.python.ideep_test_util as mu


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),
           **mu.gcs)
    def test_MomentumSGDUpdate(self, n, nesterov, gc, dc):
        param = np.random.rand(n).astype(np.float32)

            

Reported by Pylint.

              class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),
           **mu.gcs)
    def test_MomentumSGDUpdate(self, n, nesterov, gc, dc):
        param = np.random.rand(n).astype(np.float32)
        grad = np.random.rand(n).astype(np.float32)
        lr = np.random.rand(1).astype(np.float32)
        param_momentum = np.random.rand(n).astype(np.float32)
        momentum = 0.9

            

Reported by Pylint.

              




import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu

            

Reported by Pylint.

              
import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu


            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),

            

Reported by Pylint.

              

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),
           **mu.gcs)
    def test_MomentumSGDUpdate(self, n, nesterov, gc, dc):
        param = np.random.rand(n).astype(np.float32)
        grad = np.random.rand(n).astype(np.float32)

            

Reported by Pylint.

              class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),
           **mu.gcs)
    def test_MomentumSGDUpdate(self, n, nesterov, gc, dc):
        param = np.random.rand(n).astype(np.float32)
        grad = np.random.rand(n).astype(np.float32)
        lr = np.random.rand(1).astype(np.float32)
        param_momentum = np.random.rand(n).astype(np.float32)
        momentum = 0.9

            

Reported by Pylint.

              class TestMomentumSGDUpdateOps(hu.HypothesisTestCase):
    @given(n=st.integers(4, 8), nesterov=st.booleans(),
           **mu.gcs)
    def test_MomentumSGDUpdate(self, n, nesterov, gc, dc):
        param = np.random.rand(n).astype(np.float32)
        grad = np.random.rand(n).astype(np.float32)
        lr = np.random.rand(1).astype(np.float32)
        param_momentum = np.random.rand(n).astype(np.float32)
        momentum = 0.9

            

Reported by Pylint.