1 files changed, 0 insertions, 97 deletions
diff --git a/libs/tqdm/tests/tests_keras.py b/libs/tqdm/tests/tests_keras.py
deleted file mode 100644
index 11684c490..000000000
--- a/libs/tqdm/tests/tests_keras.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from __future__ import division
-from tqdm import tqdm
-from tests_tqdm import with_setup, pretest, posttest, SkipTest, StringIO, \
-    closing
-
-
-@with_setup(pretest, posttest)
-def test_keras():
-    """Test tqdm.keras.TqdmCallback"""
-    try:
-        from tqdm.keras import TqdmCallback
-        import numpy as np
-        try:
-            import keras as K
-        except ImportError:
-            from tensorflow import keras as K
-    except ImportError:
-        raise SkipTest
-
-    # 1D autoencoder
-    dtype = np.float32
-    model = K.models.Sequential(
-        [K.layers.InputLayer((1, 1), dtype=dtype), K.layers.Conv1D(1, 1)]
-    )
-    model.compile("adam", "mse")
-    x = np.random.rand(100, 1, 1).astype(dtype)
-    batch_size = 10
-    batches = len(x) / batch_size
-    epochs = 5
-
-    with closing(StringIO()) as our_file:
-
-        class Tqdm(tqdm):
-            """redirected I/O class"""
-
-            def __init__(self, *a, **k):
-                k.setdefault("file", our_file)
-                super(Tqdm, self).__init__(*a, **k)
-
-        # just epoch (no batch) progress
-        model.fit(
-            x,
-            x,
-            epochs=epochs,
-            batch_size=batch_size,
-            verbose=False,
-            callbacks=[
-                TqdmCallback(
-                    epochs,
-                    data_size=len(x),
-                    batch_size=batch_size,
-                    verbose=0,
-                    tqdm_class=Tqdm,
-                )
-            ],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) not in res
-
-        # full (epoch and batch) progress
-        our_file.seek(0)
-        our_file.truncate()
-        model.fit(
-            x,
-            x,
-            epochs=epochs,
-            batch_size=batch_size,
-            verbose=False,
-            callbacks=[
-                TqdmCallback(
-                    epochs,
-                    data_size=len(x),
-                    batch_size=batch_size,
-                    verbose=2,
-                    tqdm_class=Tqdm,
-                )
-            ],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) in res
-
-        # auto-detect epochs and batches
-        our_file.seek(0)
-        our_file.truncate()
-        model.fit(
-            x,
-            x,
-            epochs=epochs,
-            batch_size=batch_size,
-            verbose=False,
-            callbacks=[TqdmCallback(verbose=2, tqdm_class=Tqdm)],
-        )
-        res = our_file.getvalue()
-        assert "{epochs}/{epochs}".format(epochs=epochs) in res
-        assert "{batches}/{batches}".format(batches=batches) in res