import_utils.py 38.9 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# 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 json
import os
21
import shutil
玩火's avatar
玩火 committed
22
import subprocess
23
import sys
24
import warnings
25
from collections import OrderedDict
26
from functools import lru_cache
27
28
from itertools import chain
from types import ModuleType
29
from typing import Any, Tuple, Union
30
31
32
33

from packaging import version

from . import logging
34
from .versions import importlib_metadata
35
36
37
38


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

39

Yih-Dar's avatar
Yih-Dar committed
40
# TODO: This doesn't work for all packages (`bs4`, `faiss`, etc.) Talk to Sylvain to see how to do with it better.
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[Tuple[bool, str], bool]:
    # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
    package_exists = importlib.util.find_spec(pkg_name) is not None
    package_version = "N/A"
    if package_exists:
        try:
            package_version = importlib_metadata.version(pkg_name)
            package_exists = True
        except importlib_metadata.PackageNotFoundError:
            package_exists = False
        logger.debug(f"Detected {pkg_name} version {package_version}")
    if return_version:
        return package_exists, package_version
    else:
        return package_exists


58
59
60
61
62
63
64
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()

65
66
FORCE_TF_AVAILABLE = os.environ.get("FORCE_TF_AVAILABLE", "AUTO").upper()

67
68
69
70
71
72
73
# This is the version of torch required to run torch.fx features and torch.onnx with dictionary inputs.
TORCH_FX_REQUIRED_VERSION = version.parse("1.10")


_accelerate_available, _accelerate_version = _is_package_available("accelerate", return_version=True)
_apex_available = _is_package_available("apex")
_bitsandbytes_available = _is_package_available("bitsandbytes")
Yih-Dar's avatar
Yih-Dar committed
74
75
# `importlib_metadata.version` doesn't work with `bs4` but `beautifulsoup4`. For `importlib.util.find_spec`, reversed.
_bs4_available = importlib.util.find_spec("bs4") is not None
76
77
78
79
_coloredlogs_available = _is_package_available("coloredlogs")
_datasets_available = _is_package_available("datasets")
_decord_available = importlib.util.find_spec("decord") is not None
_detectron2_available = _is_package_available("detectron2")
Yih-Dar's avatar
Yih-Dar committed
80
81
82
83
84
85
86
87
88
89
90
# We need to check both `faiss` and `faiss-cpu`.
_faiss_available = importlib.util.find_spec("faiss") is not None
try:
    _faiss_version = importlib_metadata.version("faiss")
    logger.debug(f"Successfully imported faiss version {_faiss_version}")
except importlib_metadata.PackageNotFoundError:
    try:
        _faiss_version = importlib_metadata.version("faiss-cpu")
        logger.debug(f"Successfully imported faiss version {_faiss_version}")
    except importlib_metadata.PackageNotFoundError:
        _faiss_available = False
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
_ftfy_available = _is_package_available("ftfy")
_ipex_available, _ipex_version = _is_package_available("intel_extension_for_pytorch", return_version=True)
_jieba_available = _is_package_available("jieba")
_kenlm_available = _is_package_available("kenlm")
_keras_nlp_available = _is_package_available("keras_nlp")
_librosa_available = _is_package_available("librosa")
_natten_available = _is_package_available("natten")
_onnx_available = _is_package_available("onnx")
_openai_available = _is_package_available("openai")
_optimum_available = _is_package_available("optimum")
_pandas_available = _is_package_available("pandas")
_peft_available = _is_package_available("peft")
_phonemizer_available = _is_package_available("phonemizer")
_psutil_available = _is_package_available("psutil")
_py3nvml_available = _is_package_available("py3nvml")
_pyctcdecode_available = _is_package_available("pyctcdecode")
_pytesseract_available = _is_package_available("pytesseract")
108
_pytest_available = _is_package_available("pytest")
109
110
111
112
113
114
115
116
117
118
119
120
_pytorch_quantization_available = _is_package_available("pytorch_quantization")
_rjieba_available = _is_package_available("rjieba")
_sacremoses_available = _is_package_available("sacremoses")
_safetensors_available = _is_package_available("safetensors")
_scipy_available = _is_package_available("scipy")
_sentencepiece_available = _is_package_available("sentencepiece")
_sklearn_available = importlib.util.find_spec("sklearn") is not None
if _sklearn_available:
    try:
        importlib_metadata.version("scikit-learn")
    except importlib_metadata.PackageNotFoundError:
        _sklearn_available = False
121
_smdistributed_available = importlib.util.find_spec("smdistributed") is not None
122
123
124
125
126
127
128
129
130
131
132
133
134
_soundfile_available = _is_package_available("soundfile")
_spacy_available = _is_package_available("spacy")
_sudachipy_available = _is_package_available("sudachipy")
_tensorflow_probability_available = _is_package_available("tensorflow_probability")
_tensorflow_text_available = _is_package_available("tensorflow_text")
_tf2onnx_available = _is_package_available("tf2onnx")
_timm_available = _is_package_available("timm")
_tokenizers_available = _is_package_available("tokenizers")
_torchaudio_available = _is_package_available("torchaudio")
_torchdistx_available = _is_package_available("torchdistx")
_torchvision_available = _is_package_available("torchvision")


135
_torch_version = "N/A"
136
_torch_available = False
137
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
138
    _torch_available, _torch_version = _is_package_available("torch", return_version=True)
139
140
141
142
143
144
else:
    logger.info("Disabling PyTorch because USE_TF is set")
    _torch_available = False


_tf_version = "N/A"
145
_tf_available = False
146
147
if FORCE_TF_AVAILABLE in ENV_VARS_TRUE_VALUES:
    _tf_available = True
148
else:
149
    if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
150
        _tf_available = _is_package_available("tensorflow")
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
        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}. Transformers requires version 2 minimum."
                )
                _tf_available = False
    else:
        logger.info("Disabling Tensorflow because USE_TORCH is set")
182
183


184
185
186
187
188
189
190
ccl_version = "N/A"
_is_ccl_available = (
    importlib.util.find_spec("torch_ccl") is not None
    or importlib.util.find_spec("oneccl_bindings_for_pytorch") is not None
)
try:
    ccl_version = importlib_metadata.version("oneccl_bind_pt")
191
    logger.debug(f"Detected oneccl_bind_pt version {ccl_version}")
192
193
except importlib_metadata.PackageNotFoundError:
    _is_ccl_available = False
194

195

196
197
198
199
200
201
202
203
204
205
_flax_available = False
if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _flax_available, _flax_version = _is_package_available("flax", return_version=True)
    if _flax_available:
        _jax_available, _jax_version = _is_package_available("jax", return_version=True)
        if _jax_available:
            logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
        else:
            _flax_available = _jax_available = False
            _jax_version = _flax_version = "N/A"
206

207
208
209
210
211
212
213
214

_torch_fx_available = False
if _torch_available:
    torch_version = version.parse(_torch_version)
    _torch_fx_available = (torch_version.major, torch_version.minor) >= (
        TORCH_FX_REQUIRED_VERSION.major,
        TORCH_FX_REQUIRED_VERSION.minor,
    )
215
216


217
def is_kenlm_available():
218
    return _kenlm_available
219
220


221
222
223
224
def is_torch_available():
    return _torch_available


225
226
227
228
def get_torch_version():
    return _torch_version


NielsRogge's avatar
NielsRogge committed
229
def is_torchvision_available():
230
    return _torchvision_available
NielsRogge's avatar
NielsRogge committed
231
232


233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
def is_pyctcdecode_available():
    return _pyctcdecode_available


def is_librosa_available():
    return _librosa_available


def is_torch_cuda_available():
    if is_torch_available():
        import torch

        return torch.cuda.is_available()
    else:
        return False


250
def is_torch_bf16_gpu_available():
251
252
253
254
255
256
257
258
259
    if not is_torch_available():
        return False

    import torch

    # since currently no utility function is available we build our own.
    # some bits come from https://github.com/pytorch/pytorch/blob/2289a12f21c54da93bf5d696e3f9aea83dd9c10d/torch/testing/_internal/common_cuda.py#L51
    # with additional check for torch version
    # to succeed:
260
261
262
    # 1. torch >= 1.10 (1.9 should be enough for AMP API has changed in 1.10, so using 1.10 as minimal)
    # 2. the hardware needs to support bf16 (GPU arch >= Ampere, or CPU)
    # 3. if using gpu, CUDA >= 11
263
264
265
    # 4. torch.autocast exists
    # XXX: one problem here is that it may give invalid results on mixed gpus setup, so it's
    # really only correct for the 0th gpu (or currently set default device if different from 0)
266
    if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.10"):
267
        return False
268
269
270

    if torch.cuda.is_available() and torch.version.cuda is not None:
        if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
271
            return False
272
        if int(torch.version.cuda.split(".")[0]) < 11:
273
            return False
274
        if not hasattr(torch.cuda.amp, "autocast"):
275
            return False
276
    else:
277
278
279
280
281
282
283
284
285
286
287
        return False

    return True


def is_torch_bf16_cpu_available():
    if not is_torch_available():
        return False

    import torch

288
    if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.10"):
289
        return False
290

291
292
293
294
    try:
        # multiple levels of AttributeError depending on the pytorch version so do them all in one check
        _ = torch.cpu.amp.autocast
    except AttributeError:
295
        return False
296

297
298
299
300
    return True


def is_torch_bf16_available():
301
302
303
304
305
306
307
308
    # the original bf16 check was for gpu only, but later a cpu/bf16 combo has emerged so this util
    # has become ambiguous and therefore deprecated
    warnings.warn(
        "The util is_torch_bf16_available is deprecated, please use is_torch_bf16_gpu_available "
        "or is_torch_bf16_cpu_available instead according to whether it's used with cpu or gpu",
        FutureWarning,
    )
    return is_torch_bf16_gpu_available()
309
310
311
312
313
314
315
316
317
318
319
320
321
322


def is_torch_tf32_available():
    if not is_torch_available():
        return False

    import torch

    if not torch.cuda.is_available() or torch.version.cuda is None:
        return False
    if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
        return False
    if int(torch.version.cuda.split(".")[0]) < 11:
        return False
323
    if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.7"):
324
325
326
327
328
329
330
331
332
        return False

    return True


def is_torch_fx_available():
    return _torch_fx_available


333
def is_peft_available():
334
    return _peft_available
335
336


NielsRogge's avatar
NielsRogge committed
337
def is_bs4_available():
338
    return _bs4_available
NielsRogge's avatar
NielsRogge committed
339
340


341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
def is_tf_available():
    return _tf_available


def is_coloredlogs_available():
    return _coloredlogs_available


def is_tf2onnx_available():
    return _tf2onnx_available


def is_onnx_available():
    return _onnx_available


Sylvain Gugger's avatar
Sylvain Gugger committed
357
def is_openai_available():
358
    return _openai_available
Sylvain Gugger's avatar
Sylvain Gugger committed
359
360


361
362
363
364
365
366
367
368
def is_flax_available():
    return _flax_available


def is_ftfy_available():
    return _ftfy_available


369
@lru_cache()
370
371
def is_torch_tpu_available(check_device=True):
    "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
372
373
    if not _torch_available:
        return False
374
375
376
377
378
    if importlib.util.find_spec("torch_xla") is not None:
        if check_device:
            # We need to check if `xla_device` can be found, will raise a RuntimeError if not
            try:
                import torch_xla.core.xla_model as xm
379

380
381
382
383
                _ = xm.xla_device()
                return True
            except RuntimeError:
                return False
384
        return True
385
    return False
386
387


388
389
390
391
392
393
394
@lru_cache()
def is_torch_neuroncore_available(check_device=True):
    if importlib.util.find_spec("torch_neuronx") is not None:
        return is_torch_tpu_available(check_device)
    return False


395
def is_torchdynamo_available():
396
397
398
399
400
401
402
403
    if not is_torch_available():
        return False
    try:
        import torch._dynamo as dynamo  # noqa: F401

        return True
    except Exception:
        return False
404
405


406
407
408
409
410
411
def is_torch_compile_available():
    if not is_torch_available():
        return False

    import torch

412
413
    # We don't do any version check here to support nighlies marked as 1.14. Ultimately needs to check version against
    # 2.0 but let's do it later.
414
415
416
    return hasattr(torch, "compile")


417
418
419
420
421
422
def is_torch_tensorrt_fx_available():
    if importlib.util.find_spec("torch_tensorrt") is None:
        return False
    return importlib.util.find_spec("torch_tensorrt.fx") is not None


423
424
425
426
427
428
429
430
431
def is_datasets_available():
    return _datasets_available


def is_detectron2_available():
    return _detectron2_available


def is_rjieba_available():
432
    return _rjieba_available
433
434
435


def is_psutil_available():
436
    return _psutil_available
437
438
439


def is_py3nvml_available():
440
    return _py3nvml_available
441
442


443
def is_sacremoses_available():
444
    return _sacremoses_available
445
446


447
def is_apex_available():
448
    return _apex_available
449
450


451
def is_ninja_available():
玩火's avatar
玩火 committed
452
453
454
455
456
457
458
459
460
461
    r"""
    Code comes from *torch.utils.cpp_extension.is_ninja_available()*. Returns `True` if the
    [ninja](https://ninja-build.org/) build system is available on the system, `False` otherwise.
    """
    try:
        subprocess.check_output("ninja --version".split())
    except Exception:
        return False
    else:
        return True
462
463


464
def is_ipex_available():
465
466
467
    def get_major_and_minor_from_version(full_version):
        return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor)

468
    if not is_torch_available() or not _ipex_available:
469
        return False
470

471
472
473
474
475
476
477
478
479
    torch_major_and_minor = get_major_and_minor_from_version(_torch_version)
    ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version)
    if torch_major_and_minor != ipex_major_and_minor:
        logger.warning(
            f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
            f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."
        )
        return False
    return True
480
481


482
def is_bitsandbytes_available():
483
    return _bitsandbytes_available
484
485


486
def is_torchdistx_available():
487
    return _torchdistx_available
488
489


490
491
492
493
494
def is_faiss_available():
    return _faiss_available


def is_scipy_available():
495
    return _scipy_available
496
497
498


def is_sklearn_available():
499
    return _sklearn_available
500
501
502


def is_sentencepiece_available():
503
    return _sentencepiece_available
504
505
506
507
508
509
510
511


def is_protobuf_available():
    if importlib.util.find_spec("google") is None:
        return False
    return importlib.util.find_spec("google.protobuf") is not None


512
513
514
def is_accelerate_available(min_version: str = None):
    if min_version is not None:
        return _accelerate_available and version.parse(_accelerate_version) >= version.parse(min_version)
515
    return _accelerate_available
516
517


518
def is_optimum_available():
519
    return _optimum_available
520
521


522
def is_optimum_neuron_available():
523
    return _optimum_available and _is_package_available("optimum.neuron")
524
525


526
def is_safetensors_available():
527
528
529
    if is_torch_available() and version.parse(_torch_version) < version.parse("1.10"):
        return False
    return _safetensors_available
530
531


532
def is_tokenizers_available():
533
    return _tokenizers_available
534
535
536


def is_vision_available():
537
538
539
540
541
542
543
544
    _pil_available = importlib.util.find_spec("PIL") is not None
    if _pil_available:
        try:
            package_version = importlib_metadata.version("Pillow")
        except importlib_metadata.PackageNotFoundError:
            return False
        logger.debug(f"Detected PIL version {package_version}")
    return _pil_available
545
546
547


def is_pytesseract_available():
548
    return _pytesseract_available
549
550


551
552
553
554
def is_pytest_available():
    return _pytest_available


555
def is_spacy_available():
556
    return _spacy_available
557
558


559
def is_tensorflow_text_available():
560
    return is_tf_available() and _tensorflow_text_available
561
562


563
def is_keras_nlp_available():
564
    return is_tensorflow_text_available() and _keras_nlp_available
565
566


567
568
569
570
571
572
573
574
def is_in_notebook():
    try:
        # Test adapted from tqdm.autonotebook: https://github.com/tqdm/tqdm/blob/master/tqdm/autonotebook.py
        get_ipython = sys.modules["IPython"].get_ipython
        if "IPKernelApp" not in get_ipython().config:
            raise ImportError("console")
        if "VSCODE_PID" in os.environ:
            raise ImportError("vscode")
575
576
577
        if "DATABRICKS_RUNTIME_VERSION" in os.environ and os.environ["DATABRICKS_RUNTIME_VERSION"] < "11.0":
            # Databricks Runtime 11.0 and above uses IPython kernel by default so it should be compatible with Jupyter notebook
            # https://docs.microsoft.com/en-us/azure/databricks/notebooks/ipython-kernel
578
            raise ImportError("databricks")
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593

        return importlib.util.find_spec("IPython") is not None
    except (AttributeError, ImportError, KeyError):
        return False


def is_pytorch_quantization_available():
    return _pytorch_quantization_available


def is_tensorflow_probability_available():
    return _tensorflow_probability_available


def is_pandas_available():
594
    return _pandas_available
595
596
597
598
599
600
601
602
603
604
605
606
607


def is_sagemaker_dp_enabled():
    # Get the sagemaker specific env variable.
    sagemaker_params = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        sagemaker_params = json.loads(sagemaker_params)
        if not sagemaker_params.get("sagemaker_distributed_dataparallel_enabled", False):
            return False
    except json.JSONDecodeError:
        return False
    # Lastly, check if the `smdistributed` module is present.
608
    return _smdistributed_available
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631


def is_sagemaker_mp_enabled():
    # Get the sagemaker specific mp parameters from smp_options variable.
    smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}")
    try:
        # Parse it and check the field "partitions" is included, it is required for model parallel.
        smp_options = json.loads(smp_options)
        if "partitions" not in smp_options:
            return False
    except json.JSONDecodeError:
        return False

    # Get the sagemaker specific framework parameters from mpi_options variable.
    mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        mpi_options = json.loads(mpi_options)
        if not mpi_options.get("sagemaker_mpi_enabled", False):
            return False
    except json.JSONDecodeError:
        return False
    # Lastly, check if the `smdistributed` module is present.
632
    return _smdistributed_available
633
634
635
636
637
638
639
640
641
642
643
644
645
646


def is_training_run_on_sagemaker():
    return "SAGEMAKER_JOB_NAME" in os.environ


def is_soundfile_availble():
    return _soundfile_available


def is_timm_available():
    return _timm_available


647
648
649
650
def is_natten_available():
    return _natten_available


651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
def is_torchaudio_available():
    return _torchaudio_available


def is_speech_available():
    # For now this depends on torchaudio but the exact dependency might evolve in the future.
    return _torchaudio_available


def is_phonemizer_available():
    return _phonemizer_available


def torch_only_method(fn):
    def wrapper(*args, **kwargs):
        if not _torch_available:
            raise ImportError(
                "You need to install pytorch to use this method or class, "
                "or activate it with environment variables USE_TORCH=1 and USE_TF=0."
            )
        else:
            return fn(*args, **kwargs)

    return wrapper


677
678
679
680
def is_ccl_available():
    return _is_ccl_available


681
def is_decord_available():
682
    return _decord_available
683
684


685
def is_sudachi_available():
686
    return _sudachipy_available
687
688
689


def is_jumanpp_available():
Hao Wang's avatar
Hao Wang committed
690
    return (importlib.util.find_spec("rhoknp") is not None) and (shutil.which("jumanpp") is not None)
691
692


693
694
695
696
def is_cython_available():
    return importlib.util.find_spec("pyximport") is not None


697
698
699
700
def is_jieba_available():
    return _jieba_available


701
702
703
704
705
706
707
708
709
710
711
712
713
714
# docstyle-ignore
DATASETS_IMPORT_ERROR = """
{0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with:
```
pip install datasets
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install datasets
```
then restarting your kernel.

Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current
working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or
715
that python file if that's the case. Please note that you may need to restart your runtime after installation.
716
717
718
719
720
721
722
723
724
725
726
727
728
"""


# docstyle-ignore
TOKENIZERS_IMPORT_ERROR = """
{0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
```
pip install tokenizers
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install tokenizers
```
729
Please note that you may need to restart your runtime after installation.
730
731
732
733
734
735
736
"""


# docstyle-ignore
SENTENCEPIECE_IMPORT_ERROR = """
{0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
737
that match your environment. Please note that you may need to restart your runtime after installation.
738
739
740
741
742
743
744
"""


# docstyle-ignore
PROTOBUF_IMPORT_ERROR = """
{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
745
that match your environment. Please note that you may need to restart your runtime after installation.
746
747
748
749
750
751
752
"""


# docstyle-ignore
FAISS_IMPORT_ERROR = """
{0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
753
that match your environment. Please note that you may need to restart your runtime after installation.
754
755
756
757
758
759
760
"""


# 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.
761
Please note that you may need to restart your runtime after installation.
762
763
"""

NielsRogge's avatar
NielsRogge committed
764
765
766
767
768
769
770
771

# docstyle-ignore
TORCHVISION_IMPORT_ERROR = """
{0} requires the Torchvision 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.
Please note that you may need to restart your runtime after installation.
"""

772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
# docstyle-ignore
PYTORCH_IMPORT_ERROR_WITH_TF = """
{0} requires the PyTorch library but it was not found in your environment.
However, we were able to find a TensorFlow installation. TensorFlow classes begin
with "TF", but are otherwise identically named to our PyTorch classes. This
means that the TF equivalent of the class you tried to import would be "TF{0}".
If you want to use TensorFlow, please use TF classes instead!

If you really do want to use PyTorch please go to
https://pytorch.org/get-started/locally/ and follow the instructions that
match your environment.
"""

# docstyle-ignore
TF_IMPORT_ERROR_WITH_PYTORCH = """
{0} requires the TensorFlow library but it was not found in your environment.
However, we were able to find a PyTorch installation. PyTorch classes do not begin
with "TF", but are otherwise identically named to our TF classes.
If you want to use PyTorch, please use those classes instead!

If you really do want to use TensorFlow, please follow the instructions on the
installation page https://www.tensorflow.org/install that match your environment.
"""

NielsRogge's avatar
NielsRogge committed
796
797
798
# docstyle-ignore
BS4_IMPORT_ERROR = """
{0} requires the Beautiful Soup library but it was not found in your environment. You can install it with pip:
799
`pip install beautifulsoup4`. Please note that you may need to restart your runtime after installation.
NielsRogge's avatar
NielsRogge committed
800
801
"""

802
803
804
805
806
807
808
809
810
811
812

# docstyle-ignore
SKLEARN_IMPORT_ERROR = """
{0} requires the scikit-learn library but it was not found in your environment. You can install it with:
```
pip install -U scikit-learn
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install -U scikit-learn
```
813
Please note that you may need to restart your runtime after installation.
814
815
816
817
818
819
820
"""


# 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.
821
Please note that you may need to restart your runtime after installation.
822
823
824
825
826
827
828
"""


# docstyle-ignore
DETECTRON2_IMPORT_ERROR = """
{0} requires the detectron2 library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md and follow the ones
829
that match your environment. Please note that you may need to restart your runtime after installation.
830
831
832
833
834
835
836
"""


# 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.
837
Please note that you may need to restart your runtime after installation.
838
839
840
841
842
843
"""

# docstyle-ignore
FTFY_IMPORT_ERROR = """
{0} requires the ftfy library but it was not found in your environment. Checkout the instructions on the
installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones
844
that match your environment. Please note that you may need to restart your runtime after installation.
845
846
847
848
849
850
"""

# docstyle-ignore
PYTORCH_QUANTIZATION_IMPORT_ERROR = """
{0} requires the pytorch-quantization library but it was not found in your environment. You can install it with pip:
`pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com`
851
Please note that you may need to restart your runtime after installation.
852
853
854
855
856
"""

# docstyle-ignore
TENSORFLOW_PROBABILITY_IMPORT_ERROR = """
{0} requires the tensorflow_probability library but it was not found in your environment. You can install it with pip as
857
explained here: https://github.com/tensorflow/probability. Please note that you may need to restart your runtime after installation.
858
859
"""

860
861
862
863
# docstyle-ignore
TENSORFLOW_TEXT_IMPORT_ERROR = """
{0} requires the tensorflow_text library but it was not found in your environment. You can install it with pip as
explained here: https://www.tensorflow.org/text/guide/tf_text_intro.
864
Please note that you may need to restart your runtime after installation.
865
866
"""

867
868
869
870
871

# docstyle-ignore
PANDAS_IMPORT_ERROR = """
{0} requires the pandas library but it was not found in your environment. You can install it with pip as
explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html.
872
Please note that you may need to restart your runtime after installation.
873
874
875
876
877
878
"""


# docstyle-ignore
PHONEMIZER_IMPORT_ERROR = """
{0} requires the phonemizer library but it was not found in your environment. You can install it with pip:
879
`pip install phonemizer`. Please note that you may need to restart your runtime after installation.
880
881
882
"""


883
884
885
# docstyle-ignore
SACREMOSES_IMPORT_ERROR = """
{0} requires the sacremoses library but it was not found in your environment. You can install it with pip:
886
`pip install sacremoses`. Please note that you may need to restart your runtime after installation.
887
888
889
"""


890
891
892
# docstyle-ignore
SCIPY_IMPORT_ERROR = """
{0} requires the scipy library but it was not found in your environment. You can install it with pip:
893
`pip install scipy`. Please note that you may need to restart your runtime after installation.
894
895
896
897
898
899
"""


# docstyle-ignore
SPEECH_IMPORT_ERROR = """
{0} requires the torchaudio library but it was not found in your environment. You can install it with pip:
900
`pip install torchaudio`. Please note that you may need to restart your runtime after installation.
901
902
903
904
905
"""

# docstyle-ignore
TIMM_IMPORT_ERROR = """
{0} requires the timm library but it was not found in your environment. You can install it with pip:
906
`pip install timm`. Please note that you may need to restart your runtime after installation.
907
908
"""

909
910
911
912
913
914
915
# docstyle-ignore
NATTEN_IMPORT_ERROR = """
{0} requires the natten library but it was not found in your environment. You can install it by referring to:
shi-labs.com/natten . You can also install it with pip (may take longer to build):
`pip install natten`. Please note that you may need to restart your runtime after installation.
"""

916
917
918
# docstyle-ignore
VISION_IMPORT_ERROR = """
{0} requires the PIL library but it was not found in your environment. You can install it with pip:
919
`pip install pillow`. Please note that you may need to restart your runtime after installation.
920
921
922
923
924
925
"""


# docstyle-ignore
PYTESSERACT_IMPORT_ERROR = """
{0} requires the PyTesseract library but it was not found in your environment. You can install it with pip:
926
`pip install pytesseract`. Please note that you may need to restart your runtime after installation.
927
928
929
930
931
"""

# docstyle-ignore
PYCTCDECODE_IMPORT_ERROR = """
{0} requires the pyctcdecode library but it was not found in your environment. You can install it with pip:
932
`pip install pyctcdecode`. Please note that you may need to restart your runtime after installation.
933
934
"""

935
936
937
# docstyle-ignore
ACCELERATE_IMPORT_ERROR = """
{0} requires the accelerate library but it was not found in your environment. You can install it with pip:
938
`pip install accelerate`. Please note that you may need to restart your runtime after installation.
939
940
"""

941
942
943
944
# docstyle-ignore
CCL_IMPORT_ERROR = """
{0} requires the torch ccl library but it was not found in your environment. You can install it with pip:
`pip install oneccl_bind_pt -f https://developer.intel.com/ipex-whl-stable`
945
Please note that you may need to restart your runtime after installation.
946
"""
947

948
949
950
951
952
DECORD_IMPORT_ERROR = """
{0} requires the decord library but it was not found in your environment. You can install it with pip: `pip install
decord`. Please note that you may need to restart your runtime after installation.
"""

Clémentine Fourrier's avatar
Clémentine Fourrier committed
953
954
955
956
957
CYTHON_IMPORT_ERROR = """
{0} requires the Cython library but it was not found in your environment. You can install it with pip: `pip install
Cython`. Please note that you may need to restart your runtime after installation.
"""

958
959
960
961
962
JIEBA_IMPORT_ERROR = """
{0} requires the jieba library but it was not found in your environment. You can install it with pip: `pip install
jieba`. Please note that you may need to restart your runtime after installation.
"""

963
964
BACKENDS_MAPPING = OrderedDict(
    [
NielsRogge's avatar
NielsRogge committed
965
        ("bs4", (is_bs4_available, BS4_IMPORT_ERROR)),
966
967
968
969
970
971
972
973
974
975
        ("datasets", (is_datasets_available, DATASETS_IMPORT_ERROR)),
        ("detectron2", (is_detectron2_available, DETECTRON2_IMPORT_ERROR)),
        ("faiss", (is_faiss_available, FAISS_IMPORT_ERROR)),
        ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
        ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)),
        ("pandas", (is_pandas_available, PANDAS_IMPORT_ERROR)),
        ("phonemizer", (is_phonemizer_available, PHONEMIZER_IMPORT_ERROR)),
        ("protobuf", (is_protobuf_available, PROTOBUF_IMPORT_ERROR)),
        ("pyctcdecode", (is_pyctcdecode_available, PYCTCDECODE_IMPORT_ERROR)),
        ("pytesseract", (is_pytesseract_available, PYTESSERACT_IMPORT_ERROR)),
976
        ("sacremoses", (is_sacremoses_available, SACREMOSES_IMPORT_ERROR)),
977
978
979
980
981
982
        ("pytorch_quantization", (is_pytorch_quantization_available, PYTORCH_QUANTIZATION_IMPORT_ERROR)),
        ("sentencepiece", (is_sentencepiece_available, SENTENCEPIECE_IMPORT_ERROR)),
        ("sklearn", (is_sklearn_available, SKLEARN_IMPORT_ERROR)),
        ("speech", (is_speech_available, SPEECH_IMPORT_ERROR)),
        ("tensorflow_probability", (is_tensorflow_probability_available, TENSORFLOW_PROBABILITY_IMPORT_ERROR)),
        ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
983
        ("tensorflow_text", (is_tensorflow_text_available, TENSORFLOW_TEXT_IMPORT_ERROR)),
984
        ("timm", (is_timm_available, TIMM_IMPORT_ERROR)),
985
        ("natten", (is_natten_available, NATTEN_IMPORT_ERROR)),
986
987
        ("tokenizers", (is_tokenizers_available, TOKENIZERS_IMPORT_ERROR)),
        ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
NielsRogge's avatar
NielsRogge committed
988
        ("torchvision", (is_torchvision_available, TORCHVISION_IMPORT_ERROR)),
989
990
        ("vision", (is_vision_available, VISION_IMPORT_ERROR)),
        ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
991
        ("accelerate", (is_accelerate_available, ACCELERATE_IMPORT_ERROR)),
992
        ("oneccl_bind_pt", (is_ccl_available, CCL_IMPORT_ERROR)),
993
        ("decord", (is_decord_available, DECORD_IMPORT_ERROR)),
Clémentine Fourrier's avatar
Clémentine Fourrier committed
994
        ("cython", (is_cython_available, CYTHON_IMPORT_ERROR)),
995
        ("jieba", (is_jieba_available, JIEBA_IMPORT_ERROR)),
996
997
998
999
1000
1001
1002
1003
1004
    ]
)


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

    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
1005
1006
1007
1008
1009
1010
1011
1012
1013

    # Raise an error for users who might not realize that classes without "TF" are torch-only
    if "torch" in backends and "tf" not in backends and not is_torch_available() and is_tf_available():
        raise ImportError(PYTORCH_IMPORT_ERROR_WITH_TF.format(name))

    # Raise the inverse error for PyTorch users trying to load TF classes
    if "tf" in backends and "torch" not in backends and is_torch_available() and not is_tf_available():
        raise ImportError(TF_IMPORT_ERROR_WITH_PYTORCH.format(name))

1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
    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.
    """

1026
    def __getattribute__(cls, key):
1027
        if key.startswith("_") and key != "_from_config":
1028
            return super().__getattribute__(key)
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
        requires_backends(cls, cls._backends)


def is_torch_fx_proxy(x):
    if is_torch_fx_available():
        import torch.fx

        return isinstance(x, torch.fx.Proxy)
    return False


class _LazyModule(ModuleType):
    """
    Module class that surfaces all objects but only performs associated imports when the objects are requested.
    """

    # Very heavily inspired by optuna.integration._IntegrationModule
    # https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
    def __init__(self, name, module_file, import_structure, module_spec=None, extra_objects=None):
        super().__init__(name)
        self._modules = set(import_structure.keys())
        self._class_to_module = {}
        for key, values in import_structure.items():
            for value in values:
                self._class_to_module[value] = key
        # Needed for autocompletion in an IDE
        self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))
        self.__file__ = module_file
        self.__spec__ = module_spec
        self.__path__ = [os.path.dirname(module_file)]
        self._objects = {} if extra_objects is None else extra_objects
        self._name = name
        self._import_structure = import_structure

    # Needed for autocompletion in an IDE
    def __dir__(self):
        result = super().__dir__()
        # The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
        # they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.
        for attr in self.__all__:
            if attr not in result:
                result.append(attr)
        return result

    def __getattr__(self, name: str) -> Any:
        if name in self._objects:
            return self._objects[name]
        if name in self._modules:
            value = self._get_module(name)
        elif name in self._class_to_module.keys():
            module = self._get_module(self._class_to_module[name])
            value = getattr(module, name)
        else:
            raise AttributeError(f"module {self.__name__} has no attribute {name}")

        setattr(self, name, value)
        return value

    def _get_module(self, module_name: str):
        try:
            return importlib.import_module("." + module_name, self.__name__)
        except Exception as e:
            raise RuntimeError(
Sylvain Gugger's avatar
Sylvain Gugger committed
1092
1093
                f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
                f" traceback):\n{e}"
1094
1095
1096
1097
            ) from e

    def __reduce__(self):
        return (self.__class__, (self._name, self.__file__, self._import_structure))
1098
1099
1100
1101


class OptionalDependencyNotAvailable(BaseException):
    """Internally used error class for signalling an optional dependency was not found."""
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120


def direct_transformers_import(path: str, file="__init__.py") -> ModuleType:
    """Imports transformers directly

    Args:
        path (`str`): The path to the source file
        file (`str`, optional): The file to join with the path. Defaults to "__init__.py".

    Returns:
        `ModuleType`: The resulting imported module
    """
    name = "transformers"
    location = os.path.join(path, file)
    spec = importlib.util.spec_from_file_location(name, location, submodule_search_locations=[path])
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    module = sys.modules[name]
    return module