File size: 9,242 Bytes
d77a781
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
# Copyright 2022 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 utilities: Utilities related to imports and our lazy inits.
"""
import importlib.util
import os
import sys
from collections import OrderedDict

from packaging import version

from . import logging


# The package importlib_metadata is in a different place, depending on the python version.
if sys.version_info < (3, 8):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})

USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()

_torch_version = "N/A"
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    _torch_available = importlib.util.find_spec("torch") is not None
    if _torch_available:
        try:
            _torch_version = importlib_metadata.version("torch")
            logger.info(f"PyTorch version {_torch_version} available.")
        except importlib_metadata.PackageNotFoundError:
            _torch_available = False
else:
    logger.info("Disabling PyTorch because USE_TF is set")
    _torch_available = False


_tf_version = "N/A"
if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    _tf_available = importlib.util.find_spec("tensorflow") is not None
    if _tf_available:
        candidates = (
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "intel-tensorflow-avx512",
            "tensorflow-rocm",
            "tensorflow-macos",
            "tensorflow-aarch64",
        )
        _tf_version = None
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for pkg in candidates:
            try:
                _tf_version = importlib_metadata.version(pkg)
                break
            except importlib_metadata.PackageNotFoundError:
                pass
        _tf_available = _tf_version is not None
    if _tf_available:
        if version.parse(_tf_version) < version.parse("2"):
            logger.info(f"TensorFlow found but with version {_tf_version}. Diffusers requires version 2 minimum.")
            _tf_available = False
        else:
            logger.info(f"TensorFlow version {_tf_version} available.")
else:
    logger.info("Disabling Tensorflow because USE_TORCH is set")
    _tf_available = False


if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None
    if _flax_available:
        try:
            _jax_version = importlib_metadata.version("jax")
            _flax_version = importlib_metadata.version("flax")
            logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
        except importlib_metadata.PackageNotFoundError:
            _flax_available = False
else:
    _flax_available = False


_transformers_available = importlib.util.find_spec("transformers") is not None
try:
    _transformers_version = importlib_metadata.version("transformers")
    logger.debug(f"Successfully imported transformers version {_transformers_version}")
except importlib_metadata.PackageNotFoundError:
    _transformers_available = False


_inflect_available = importlib.util.find_spec("inflect") is not None
try:
    _inflect_version = importlib_metadata.version("inflect")
    logger.debug(f"Successfully imported inflect version {_inflect_version}")
except importlib_metadata.PackageNotFoundError:
    _inflect_available = False


_unidecode_available = importlib.util.find_spec("unidecode") is not None
try:
    _unidecode_version = importlib_metadata.version("unidecode")
    logger.debug(f"Successfully imported unidecode version {_unidecode_version}")
except importlib_metadata.PackageNotFoundError:
    _unidecode_available = False


_modelcards_available = importlib.util.find_spec("modelcards") is not None
try:
    _modelcards_version = importlib_metadata.version("modelcards")
    logger.debug(f"Successfully imported modelcards version {_modelcards_version}")
except importlib_metadata.PackageNotFoundError:
    _modelcards_available = False


_onnx_available = importlib.util.find_spec("onnxruntime") is not None
try:
    _onnxruntime_version = importlib_metadata.version("onnxruntime")
    logger.debug(f"Successfully imported onnxruntime version {_onnxruntime_version}")
except importlib_metadata.PackageNotFoundError:
    _onnx_available = False


_scipy_available = importlib.util.find_spec("scipy") is not None
try:
    _scipy_version = importlib_metadata.version("scipy")
    logger.debug(f"Successfully imported transformers version {_scipy_version}")
except importlib_metadata.PackageNotFoundError:
    _scipy_available = False


def is_torch_available():
    return _torch_available


def is_tf_available():
    return _tf_available


def is_flax_available():
    return _flax_available


def is_transformers_available():
    return _transformers_available


def is_inflect_available():
    return _inflect_available


def is_unidecode_available():
    return _unidecode_available


def is_modelcards_available():
    return _modelcards_available


def is_onnx_available():
    return _onnx_available


def is_scipy_available():
    return _scipy_available


# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""

# docstyle-ignore
INFLECT_IMPORT_ERROR = """
{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
inflect`
"""

# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""

# docstyle-ignore
ONNX_IMPORT_ERROR = """
{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
install onnxruntime`
"""

# docstyle-ignore
SCIPY_IMPORT_ERROR = """
{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
scipy`
"""

# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = """
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
"""

# docstyle-ignore
TRANSFORMERS_IMPORT_ERROR = """
{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
install transformers`
"""

# docstyle-ignore
UNIDECODE_IMPORT_ERROR = """
{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
Unidecode`
"""


BACKENDS_MAPPING = OrderedDict(
    [
        ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
        ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
        ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
        ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
        ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
        ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
        ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
        ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
    ]
)


def requires_backends(obj, backends):
    if not isinstance(backends, (list, tuple)):
        backends = [backends]

    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
    checks = (BACKENDS_MAPPING[backend] for backend in backends)
    failed = [msg.format(name) for available, msg in checks if not available()]
    if failed:
        raise ImportError("".join(failed))


class DummyObject(type):
    """
    Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
    `requires_backend` each time a user tries to access any method of that class.
    """

    def __getattr__(cls, key):
        if key.startswith("_"):
            return super().__getattr__(cls, key)
        requires_backends(cls, cls._backends)