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* Fix * Style * Name * Fix tests * Style * Remove embed sizes checking * Disable some tests * Fix * Apply suggestion
259 lines
12 KiB
Python
259 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import unittest
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from transformers import TransfoXLConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers import (
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TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFTransfoXLForSequenceClassification,
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TFTransfoXLLMHeadModel,
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TFTransfoXLModel,
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)
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class TFTransfoXLModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.mem_len = 30
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self.key_length = self.seq_length + self.mem_len
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self.clamp_len = 15
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self.is_training = True
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self.use_labels = True
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self.vocab_size = 99
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self.cutoffs = [10, 50, 80]
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self.hidden_size = 32
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self.d_embed = 32
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self.num_attention_heads = 4
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self.d_head = 8
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self.d_inner = 128
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self.div_val = 2
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self.num_hidden_layers = 5
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self.scope = None
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self.seed = 1
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self.eos_token_id = 0
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self.num_labels = 3
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self.pad_token_id = self.vocab_size - 1
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self.init_range = 0.01
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def prepare_config_and_inputs(self):
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input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = TransfoXLConfig(
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vocab_size=self.vocab_size,
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mem_len=self.mem_len,
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clamp_len=self.clamp_len,
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cutoffs=self.cutoffs,
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d_model=self.hidden_size,
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d_embed=self.d_embed,
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n_head=self.num_attention_heads,
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d_head=self.d_head,
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d_inner=self.d_inner,
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div_val=self.div_val,
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n_layer=self.num_hidden_layers,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.vocab_size - 1,
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init_range=self.init_range,
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num_labels=self.num_labels,
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)
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return (config, input_ids_1, input_ids_2, lm_labels)
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def set_seed(self):
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random.seed(self.seed)
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tf.random.set_seed(self.seed)
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def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
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model = TFTransfoXLModel(config)
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hidden_states_1, mems_1 = model(input_ids_1).to_tuple()
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inputs = {"input_ids": input_ids_2, "mems": mems_1}
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hidden_states_2, mems_2 = model(inputs).to_tuple()
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self.parent.assertEqual(hidden_states_1.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(hidden_states_2.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertListEqual(
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[mem.shape for mem in mems_1],
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[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
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)
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self.parent.assertListEqual(
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[mem.shape for mem in mems_2],
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[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
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)
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def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
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model = TFTransfoXLLMHeadModel(config)
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lm_logits_1, mems_1 = model(input_ids_1).to_tuple()
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inputs = {"input_ids": input_ids_1, "labels": lm_labels}
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_, mems_1 = model(inputs).to_tuple()
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lm_logits_2, mems_2 = model([input_ids_2, mems_1]).to_tuple()
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inputs = {"input_ids": input_ids_1, "mems": mems_1, "labels": lm_labels}
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_, mems_2 = model(inputs).to_tuple()
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self.parent.assertEqual(lm_logits_1.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertListEqual(
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[mem.shape for mem in mems_1],
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[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
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)
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self.parent.assertEqual(lm_logits_2.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertListEqual(
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[mem.shape for mem in mems_2],
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[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
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)
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def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels):
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model = TFTransfoXLForSequenceClassification(config)
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result = model(input_ids_1)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
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inputs_dict = {"input_ids": input_ids_1}
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return config, inputs_dict
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@require_tf
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class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
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)
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all_generative_model_classes = () if is_tf_available() else ()
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# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
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test_resize_embeddings = False
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test_head_masking = False
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test_onnx = False
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test_mismatched_shapes = False
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def setUp(self):
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self.model_tester = TFTransfoXLModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_transfo_xl_model(self):
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self.model_tester.set_seed()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)
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def test_transfo_xl_lm_head(self):
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self.model_tester.set_seed()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
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def test_transfo_xl_sequence_classification_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs)
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def test_model_common_attributes(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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list_other_models_with_output_ebd = [TFTransfoXLForSequenceClassification]
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for model_class in self.all_model_classes:
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model = model_class(config)
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assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
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if model_class in list_other_models_with_output_ebd:
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x = model.get_output_embeddings()
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assert isinstance(x, tf.keras.layers.Layer)
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name = model.get_bias()
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assert name is None
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else:
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x = model.get_output_embeddings()
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assert x is None
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name = model.get_bias()
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assert name is None
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def test_xla_mode(self):
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# TODO JP: Make TransfoXL XLA compliant
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFTransfoXLModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_tf
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class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase):
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@unittest.skip("Skip test until #12651 is resolved.")
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@slow
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def test_lm_generate_transfo_xl_wt103(self):
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model = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
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# fmt: off
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input_ids = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=tf.int32) # noqa: E231
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# fmt: on
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# In 1991 , the remains of Russian Tsar Nicholas II and his family
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# ( except for Alexei and Maria ) are discovered .
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# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
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# remainder of the story . 1883 Western Siberia ,
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# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
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# Rasputin has a vision and denounces one of the men as a horse thief . Although his
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# father initially slaps him for making such an accusation , Rasputin watches as the
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# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
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# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
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# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
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# fmt: off
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expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
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# fmt: on
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# In 1991, the remains of Russian Tsar Nicholas II and his family (
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# except for Alexei and Maria ) are discovered. The voice of young son,
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# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
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# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
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# and a group of men to perform magic. Rasputin has a vision and
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# denounces one of the men as a horse thief. Although his father initially
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# slaps him for making such an accusation, Rasputin watches as the man
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# is chased outside and beaten. Twenty years later, Rasputin sees a vision
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# of the Virgin Mary, prompting him to become a priest.
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# Rasputin quickly becomes famous, with people, even a bishop, begging for
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# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
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# Nicholas II and his family were discovered. The voice of <unk> young son,
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# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
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output_ids = model.generate(input_ids, max_length=200, do_sample=False)
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self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
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