348 lines
14 KiB
Python
348 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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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 copy
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import json
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import os
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import shutil
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import tempfile
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import unittest
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import unittest.mock
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from huggingface_hub import Repository, delete_repo, login
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from requests.exceptions import HTTPError
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from transformers import AutoConfig, BertConfig, GPT2Config, is_torch_available
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from transformers.configuration_utils import PretrainedConfig
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from transformers.testing_utils import PASS, USER, is_staging_test
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config_common_kwargs = {
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"return_dict": False,
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"output_hidden_states": True,
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"output_attentions": True,
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"torchscript": True,
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"torch_dtype": "float16",
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"use_bfloat16": True,
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"pruned_heads": {"a": 1},
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"tie_word_embeddings": False,
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"is_decoder": True,
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"cross_attention_hidden_size": 128,
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"add_cross_attention": True,
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"tie_encoder_decoder": True,
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"max_length": 50,
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"min_length": 3,
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"do_sample": True,
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"early_stopping": True,
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"num_beams": 3,
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"num_beam_groups": 3,
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"diversity_penalty": 0.5,
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"temperature": 2.0,
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"top_k": 10,
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"top_p": 0.7,
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"repetition_penalty": 0.8,
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"length_penalty": 0.8,
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"no_repeat_ngram_size": 5,
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"encoder_no_repeat_ngram_size": 5,
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"bad_words_ids": [1, 2, 3],
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"num_return_sequences": 3,
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"chunk_size_feed_forward": 5,
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"output_scores": True,
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"return_dict_in_generate": True,
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"forced_bos_token_id": 2,
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"forced_eos_token_id": 3,
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"remove_invalid_values": True,
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"architectures": ["BertModel"],
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"finetuning_task": "translation",
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"id2label": {0: "label"},
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"label2id": {"label": "0"},
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"tokenizer_class": "BertTokenizerFast",
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"prefix": "prefix",
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"bos_token_id": 6,
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"pad_token_id": 7,
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"eos_token_id": 8,
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"sep_token_id": 9,
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"decoder_start_token_id": 10,
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"task_specific_params": {"translation": "some_params"},
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"problem_type": "regression",
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}
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class ConfigTester(object):
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def __init__(self, parent, config_class=None, has_text_modality=True, **kwargs):
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self.parent = parent
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self.config_class = config_class
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self.has_text_modality = has_text_modality
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self.inputs_dict = kwargs
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def create_and_test_config_common_properties(self):
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config = self.config_class(**self.inputs_dict)
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common_properties = ["hidden_size", "num_attention_heads", "num_hidden_layers"]
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# Add common fields for text models
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if self.has_text_modality:
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common_properties.extend(["vocab_size"])
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# Test that config has the common properties as getters
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for prop in common_properties:
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self.parent.assertTrue(hasattr(config, prop), msg=f"`{prop}` does not exist")
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# Test that config has the common properties as setter
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for idx, name in enumerate(common_properties):
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try:
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setattr(config, name, idx)
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self.parent.assertEqual(
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getattr(config, name), idx, msg=f"`{name} value {idx} expected, but was {getattr(config, name)}"
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)
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except NotImplementedError:
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# Some models might not be able to implement setters for common_properties
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# In that case, a NotImplementedError is raised
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pass
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# Test if config class can be called with Config(prop_name=..)
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for idx, name in enumerate(common_properties):
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try:
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config = self.config_class(**{name: idx})
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self.parent.assertEqual(
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getattr(config, name), idx, msg=f"`{name} value {idx} expected, but was {getattr(config, name)}"
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)
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except NotImplementedError:
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# Some models might not be able to implement setters for common_properties
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# In that case, a NotImplementedError is raised
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pass
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def create_and_test_config_to_json_string(self):
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config = self.config_class(**self.inputs_dict)
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obj = json.loads(config.to_json_string())
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for key, value in self.inputs_dict.items():
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self.parent.assertEqual(obj[key], value)
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def create_and_test_config_to_json_file(self):
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config_first = self.config_class(**self.inputs_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "config.json")
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config_first.to_json_file(json_file_path)
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config_second = self.config_class.from_json_file(json_file_path)
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self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
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def create_and_test_config_from_and_save_pretrained(self):
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config_first = self.config_class(**self.inputs_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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config_first.save_pretrained(tmpdirname)
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config_second = self.config_class.from_pretrained(tmpdirname)
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self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
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def create_and_test_config_with_num_labels(self):
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config = self.config_class(**self.inputs_dict, num_labels=5)
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self.parent.assertEqual(len(config.id2label), 5)
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self.parent.assertEqual(len(config.label2id), 5)
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config.num_labels = 3
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self.parent.assertEqual(len(config.id2label), 3)
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self.parent.assertEqual(len(config.label2id), 3)
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def check_config_can_be_init_without_params(self):
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if self.config_class.is_composition:
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return
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config = self.config_class()
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self.parent.assertIsNotNone(config)
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def check_config_arguments_init(self):
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kwargs = copy.deepcopy(config_common_kwargs)
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config = self.config_class(**kwargs)
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wrong_values = []
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for key, value in config_common_kwargs.items():
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if key == "torch_dtype":
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if not is_torch_available():
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continue
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else:
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import torch
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if config.torch_dtype != torch.float16:
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wrong_values.append(("torch_dtype", config.torch_dtype, torch.float16))
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elif getattr(config, key) != value:
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wrong_values.append((key, getattr(config, key), value))
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if len(wrong_values) > 0:
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errors = "\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values])
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raise ValueError(f"The following keys were not properly set in the config:\n{errors}")
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def run_common_tests(self):
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self.create_and_test_config_common_properties()
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self.create_and_test_config_to_json_string()
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self.create_and_test_config_to_json_file()
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self.create_and_test_config_from_and_save_pretrained()
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self.create_and_test_config_with_num_labels()
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self.check_config_can_be_init_without_params()
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self.check_config_arguments_init()
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class FakeConfig(PretrainedConfig):
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def __init__(self, attribute=1, **kwargs):
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self.attribute = attribute
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super().__init__(**kwargs)
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# Make sure this is synchronized with the config above.
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FAKE_CONFIG_CODE = """
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from transformers import PretrainedConfig
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class FakeConfig(PretrainedConfig):
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def __init__(self, attribute=1, **kwargs):
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self.attribute = attribute
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super().__init__(**kwargs)
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"""
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@is_staging_test
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class ConfigPushToHubTester(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls._token = login(username=USER, password=PASS)
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@classmethod
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def tearDownClass(cls):
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try:
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delete_repo(token=cls._token, name="test-config")
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except HTTPError:
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pass
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try:
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delete_repo(token=cls._token, name="test-config-org", organization="valid_org")
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except HTTPError:
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pass
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try:
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delete_repo(token=cls._token, name="test-dynamic-config")
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except HTTPError:
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pass
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def test_push_to_hub(self):
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config = BertConfig(
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vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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config.save_pretrained(os.path.join(tmp_dir, "test-config"), push_to_hub=True, use_auth_token=self._token)
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new_config = BertConfig.from_pretrained(f"{USER}/test-config")
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for k, v in config.__dict__.items():
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if k != "transformers_version":
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self.assertEqual(v, getattr(new_config, k))
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def test_push_to_hub_in_organization(self):
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config = BertConfig(
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vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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config.save_pretrained(
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os.path.join(tmp_dir, "test-config-org"),
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push_to_hub=True,
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use_auth_token=self._token,
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organization="valid_org",
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)
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new_config = BertConfig.from_pretrained("valid_org/test-config-org")
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for k, v in config.__dict__.items():
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if k != "transformers_version":
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self.assertEqual(v, getattr(new_config, k))
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def test_push_to_hub_dynamic_config(self):
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config = FakeConfig(attribute=42)
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config.auto_map = {"AutoConfig": "configuration.FakeConfig"}
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with tempfile.TemporaryDirectory() as tmp_dir:
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repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-config", use_auth_token=self._token)
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config.save_pretrained(tmp_dir)
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with open(os.path.join(tmp_dir, "configuration.py"), "w") as f:
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f.write(FAKE_CONFIG_CODE)
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repo.push_to_hub()
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new_config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-config", trust_remote_code=True)
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# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
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self.assertEqual(new_config.__class__.__name__, "FakeConfig")
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self.assertEqual(new_config.attribute, 42)
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class ConfigTestUtils(unittest.TestCase):
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def test_config_from_string(self):
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c = GPT2Config()
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# attempt to modify each of int/float/bool/str config records and verify they were updated
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n_embd = c.n_embd + 1 # int
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resid_pdrop = c.resid_pdrop + 1.0 # float
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scale_attn_weights = not c.scale_attn_weights # bool
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summary_type = c.summary_type + "foo" # str
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c.update_from_string(
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f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"
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)
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self.assertEqual(n_embd, c.n_embd, "mismatch for key: n_embd")
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self.assertEqual(resid_pdrop, c.resid_pdrop, "mismatch for key: resid_pdrop")
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self.assertEqual(scale_attn_weights, c.scale_attn_weights, "mismatch for key: scale_attn_weights")
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self.assertEqual(summary_type, c.summary_type, "mismatch for key: summary_type")
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def test_config_common_kwargs_is_complete(self):
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base_config = PretrainedConfig()
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missing_keys = [key for key in base_config.__dict__ if key not in config_common_kwargs]
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# If this part of the test fails, you have arguments to addin config_common_kwargs above.
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self.assertListEqual(missing_keys, ["is_encoder_decoder", "_name_or_path", "transformers_version"])
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keys_with_defaults = [key for key, value in config_common_kwargs.items() if value == getattr(base_config, key)]
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if len(keys_with_defaults) > 0:
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raise ValueError(
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"The following keys are set with the default values in `test_configuration_common.config_common_kwargs` "
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f"pick another value for them: {', '.join(keys_with_defaults)}."
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)
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class ConfigurationVersioningTest(unittest.TestCase):
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def test_local_versioning(self):
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configuration = AutoConfig.from_pretrained("bert-base-cased")
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with tempfile.TemporaryDirectory() as tmp_dir:
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configuration.save_pretrained(tmp_dir)
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configuration.hidden_size = 2
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json.dump(configuration.to_dict(), open(os.path.join(tmp_dir, "config.4.0.0.json"), "w"))
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# This should pick the new configuration file as the version of Transformers is > 4.0.0
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new_configuration = AutoConfig.from_pretrained(tmp_dir)
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self.assertEqual(new_configuration.hidden_size, 2)
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# Will need to be adjusted if we reach v42 and this test is still here.
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# Should pick the old configuration file as the version of Transformers is < 4.42.0
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shutil.move(os.path.join(tmp_dir, "config.4.0.0.json"), os.path.join(tmp_dir, "config.42.0.0.json"))
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new_configuration = AutoConfig.from_pretrained(tmp_dir)
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self.assertEqual(new_configuration.hidden_size, 768)
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def test_repo_versioning_before(self):
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# This repo has two configuration files, one for v5.0.0 and above with an added token, one for versions lower.
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repo = "microsoft/layoutxlm-base"
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import transformers as new_transformers
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new_transformers.configuration_utils.__version__ = "v5.0.0"
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new_configuration = new_transformers.models.auto.AutoConfig.from_pretrained(repo)
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self.assertEqual(new_configuration.tokenizer_class, None)
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# Testing an older version by monkey-patching the version in the module it's used.
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import transformers as old_transformers
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old_transformers.configuration_utils.__version__ = "v3.0.0"
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old_configuration = old_transformers.models.auto.AutoConfig.from_pretrained(repo)
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self.assertEqual(old_configuration.tokenizer_class, "XLMRobertaTokenizer")
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