449 lines
16 KiB
Python
449 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. 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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"""Testing suite for the PyTorch Bros model."""
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import copy
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import unittest
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
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from transformers.utils import is_torch_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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BrosConfig,
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BrosForTokenClassification,
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BrosModel,
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BrosSpadeEEForTokenClassification,
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BrosSpadeELForTokenClassification,
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)
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class BrosModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_bbox_first_token_mask=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=64,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_bbox_first_token_mask = use_bbox_first_token_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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bbox = ids_tensor([self.batch_size, self.seq_length, 8], 1)
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# Ensure that bbox is legal
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for i in range(bbox.shape[0]):
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for j in range(bbox.shape[1]):
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if bbox[i, j, 3] < bbox[i, j, 1]:
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t = bbox[i, j, 3]
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bbox[i, j, 3] = bbox[i, j, 1]
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bbox[i, j, 1] = t
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if bbox[i, j, 2] < bbox[i, j, 0]:
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t = bbox[i, j, 2]
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bbox[i, j, 2] = bbox[i, j, 0]
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bbox[i, j, 0] = t
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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bbox_first_token_mask = None
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if self.use_bbox_first_token_mask:
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bbox_first_token_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.bool).to(torch_device)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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token_labels = None
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if self.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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initial_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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subsequent_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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config = self.get_config()
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return (
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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)
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def get_config(self):
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return BrosConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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):
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model = BrosModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, bbox=bbox, token_type_ids=token_type_ids)
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result = model(input_ids, bbox=bbox)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_token_classification(
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self,
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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):
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config.num_labels = self.num_labels
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model = BrosForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_spade_ee_token_classification(
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self,
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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):
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config.num_labels = self.num_labels
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model = BrosSpadeEEForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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bbox=bbox,
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attention_mask=input_mask,
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bbox_first_token_mask=bbox_first_token_mask,
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token_type_ids=token_type_ids,
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initial_token_labels=token_labels,
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subsequent_token_labels=token_labels,
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)
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self.parent.assertEqual(result.initial_token_logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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self.parent.assertEqual(
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result.subsequent_token_logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1)
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)
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def create_and_check_for_spade_el_token_classification(
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self,
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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):
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config.num_labels = self.num_labels
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model = BrosSpadeELForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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bbox=bbox,
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attention_mask=input_mask,
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bbox_first_token_mask=bbox_first_token_mask,
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token_type_ids=token_type_ids,
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labels=token_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1))
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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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(
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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bbox_first_token_mask,
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token_labels,
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initial_token_labels,
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subsequent_token_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"bbox": bbox,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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@require_torch
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class BrosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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test_pruning = False
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test_torchscript = False
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test_mismatched_shapes = False
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all_model_classes = (
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(
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BrosForTokenClassification,
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BrosSpadeEEForTokenClassification,
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BrosSpadeELForTokenClassification,
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BrosModel,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = () if is_torch_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": BrosModel, "token-classification": BrosForTokenClassification}
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if is_torch_available()
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else {}
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)
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# BROS requires `bbox` in the inputs which doesn't fit into the above 2 pipelines' input formats.
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# see https://github.com/huggingface/transformers/pull/26294
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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return True
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def setUp(self):
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self.model_tester = BrosModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BrosConfig, hidden_size=37)
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if return_labels:
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if model_class.__name__ in ["BrosForTokenClassification", "BrosSpadeELForTokenClassification"]:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["bbox_first_token_mask"] = torch.ones(
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[self.model_tester.batch_size, self.model_tester.seq_length],
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dtype=torch.bool,
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device=torch_device,
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)
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elif model_class.__name__ in ["BrosSpadeEEForTokenClassification"]:
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inputs_dict["initial_token_labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["subsequent_token_labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["bbox_first_token_mask"] = torch.ones(
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[self.model_tester.batch_size, self.model_tester.seq_length],
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dtype=torch.bool,
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device=torch_device,
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)
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return inputs_dict
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_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_model(*config_and_inputs)
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@require_torch_multi_gpu
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def test_multi_gpu_data_parallel_forward(self):
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super().test_multi_gpu_data_parallel_forward()
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_token_classification(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_for_token_classification(*config_and_inputs)
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def test_for_spade_ee_token_classification(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_for_spade_ee_token_classification(*config_and_inputs)
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def test_for_spade_el_token_classification(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_for_spade_el_token_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "jinho8345/bros-base-uncased"
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model = BrosModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def prepare_bros_batch_inputs():
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attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
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bbox = torch.tensor(
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[
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[
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[0.0000, 0.0000, 0.0000, 0.0000],
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[0.5223, 0.5590, 0.5787, 0.5720],
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[0.5853, 0.5590, 0.6864, 0.5720],
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[0.5853, 0.5590, 0.6864, 0.5720],
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[0.1234, 0.5700, 0.2192, 0.5840],
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[0.2231, 0.5680, 0.2782, 0.5780],
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[0.2874, 0.5670, 0.3333, 0.5780],
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[0.3425, 0.5640, 0.4344, 0.5750],
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[0.0866, 0.7770, 0.1181, 0.7870],
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[0.1168, 0.7770, 0.1522, 0.7850],
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[0.1535, 0.7750, 0.1864, 0.7850],
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[0.1890, 0.7750, 0.2572, 0.7850],
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[1.0000, 1.0000, 1.0000, 1.0000],
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],
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[
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[0.0000, 0.0000, 0.0000, 0.0000],
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[0.4396, 0.6720, 0.4659, 0.6850],
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[0.4698, 0.6720, 0.4843, 0.6850],
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[0.1575, 0.6870, 0.2021, 0.6980],
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[0.2047, 0.6870, 0.2730, 0.7000],
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[0.1299, 0.7010, 0.1430, 0.7140],
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[0.1299, 0.7010, 0.1430, 0.7140],
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[0.1562, 0.7010, 0.2441, 0.7120],
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[0.1562, 0.7010, 0.2441, 0.7120],
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[0.2454, 0.7010, 0.3150, 0.7120],
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[0.3176, 0.7010, 0.3320, 0.7110],
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[0.3333, 0.7000, 0.4029, 0.7140],
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[1.0000, 1.0000, 1.0000, 1.0000],
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],
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]
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)
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input_ids = torch.tensor(
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[
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[101, 1055, 8910, 1012, 5719, 3296, 5366, 3378, 2146, 2846, 10807, 13494, 102],
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[101, 2112, 1997, 3671, 6364, 1019, 1012, 5057, 1011, 4646, 2030, 2974, 102],
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]
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)
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return input_ids, bbox, attention_mask
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@require_torch
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class BrosModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = BrosModel.from_pretrained("jinho8345/bros-base-uncased").to(torch_device)
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input_ids, bbox, attention_mask = prepare_bros_batch_inputs()
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with torch.no_grad():
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outputs = model(
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input_ids.to(torch_device),
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bbox.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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return_dict=True,
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)
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# verify the logits
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expected_shape = torch.Size((2, 13, 768))
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self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-0.3074, 0.1363, 0.3143], [0.0925, -0.1155, 0.1050], [0.0221, 0.0003, 0.1285]]
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).to(torch_device)
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torch.set_printoptions(sci_mode=False)
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self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
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