chore: import upstream snapshot with attribution

tests/collections/common/perplexity_inputs.py

@@ -0,0 +1,49 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.  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.
+
+# Copyright 2018-2020 William Falcon
+#
+# 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.
+
+from collections import namedtuple
+
+import torch
+
+from .pl_utils import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
+
+Input = namedtuple('Input', ["probs", "logits"])
+
+
+ONLY_PROBS = Input(probs=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES), logits=None)
+
+ONLY_LOGITS1 = Input(probs=None, logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES))
+
+ONLY_LOGITS100 = Input(probs=None, logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES) * 200 - 100)
+
+PROBS_AND_LOGITS = Input(
+    probs=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES),
+    logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES) * 200 - 100,
+)
+
+NO_PROBS_NO_LOGITS = Input(probs=None, logits=None)