Remove missplaced test file (#23275)

src/transformers/data/test_generation_utils.py

-# Copyright 2020 The HuggingFace Team. 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.
-
-import random
-import unittest
-
-import timeout_decorator
-
-from ..testing_utils import require_torch
-from ..utils import cached_property, is_torch_available
-
-
-if is_torch_available():
-    import torch
-
-    from ..models.marian import MarianConfig, MarianMTModel
-
-
-@require_torch
-class GenerationUtilsTest(unittest.TestCase):
-    @cached_property
-    def config(self):
-        config = MarianConfig.from_pretrained("sshleifer/tiny-marian-en-de")
-        return config
-
-    @cached_property
-    def model(self):
-        return MarianMTModel(self.config)
-
-    def test_postprocess_next_token_scores(self):
-        config = self.config
-        model = self.model
-        # Initialize an input id tensor with batch size 8 and sequence length 12
-        input_ids = torch.arange(0, 96, 1).view((8, 12))
-        eos = config.eos_token_id
-        bad_words_ids_test_cases = [[[299]], [[23, 24], [54]], [[config.eos_token_id]], []]
-        masked_scores = [
-            [(0, 299), (1, 299), (2, 299), (3, 299), (4, 299), (5, 299), (6, 299), (7, 299)],
-            [(1, 24), (0, 54), (1, 54), (2, 54), (3, 54), (4, 54), (5, 54), (6, 54), (7, 54)],
-            [(0, eos), (1, eos), (2, eos), (3, eos), (4, eos), (5, eos), (6, eos), (7, eos)],
-            [],
-        ]
-
-        for test_case_index, bad_words_ids in enumerate(bad_words_ids_test_cases):
-            # Initialize a scores tensor with batch size 8 and vocabulary size 300
-            scores = torch.rand((8, 300))
-            output = model.postprocess_next_token_scores(
-                scores,
-                input_ids,
-                0,
-                bad_words_ids,
-                13,
-                15,
-                config.max_length,
-                config.eos_token_id,
-                config.repetition_penalty,
-                32,
-                5,
-            )
-            for masked_score in masked_scores[test_case_index]:
-                self.assertTrue(output[masked_score[0], masked_score[1]] == -float("inf"))
-
-    @timeout_decorator.timeout(10)
-    def test_postprocess_next_token_scores_large_bad_words_list(self):
-        config = self.config
-        model = self.model
-        # Initialize an input id tensor with batch size 8 and sequence length 12
-        input_ids = torch.arange(0, 96, 1).view((8, 12))
-
-        bad_words_ids = []
-        for _ in range(100):
-            length_bad_word = random.randint(1, 4)
-            bad_words_ids.append(random.sample(range(1, 300), length_bad_word))
-
-        scores = torch.rand((8, 300))
-        _ = model.postprocess_next_token_scores(
-            scores,
-            input_ids,
-            0,
-            bad_words_ids,
-            13,
-            15,
-            config.max_length,
-            config.eos_token_id,
-            config.repetition_penalty,
-            32,
-            5,
-        )