259 lines
7.5 KiB
Python
259 lines
7.5 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 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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from huggingface_hub import get_full_repo_name # for backward compatibility
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from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY as DISABLE_TELEMETRY # for backward compatibility
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from packaging import version
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from .. import __version__
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from .backbone_utils import BackboneConfigMixin, BackboneMixin
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from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
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from .doc import (
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add_code_sample_docstrings,
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add_end_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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copy_func,
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replace_return_docstrings,
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)
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from .generic import (
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ContextManagers,
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ExplicitEnum,
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ModelOutput,
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PaddingStrategy,
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TensorType,
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add_model_info_to_auto_map,
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cached_property,
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can_return_loss,
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expand_dims,
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find_labels,
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flatten_dict,
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infer_framework,
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is_jax_tensor,
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is_numpy_array,
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is_tensor,
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is_tf_symbolic_tensor,
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is_tf_tensor,
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is_torch_device,
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is_torch_dtype,
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is_torch_tensor,
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reshape,
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squeeze,
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strtobool,
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tensor_size,
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to_numpy,
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to_py_obj,
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transpose,
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working_or_temp_dir,
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)
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from .hub import (
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CLOUDFRONT_DISTRIB_PREFIX,
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HF_MODULES_CACHE,
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HUGGINGFACE_CO_PREFIX,
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HUGGINGFACE_CO_RESOLVE_ENDPOINT,
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PYTORCH_PRETRAINED_BERT_CACHE,
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PYTORCH_TRANSFORMERS_CACHE,
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S3_BUCKET_PREFIX,
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TRANSFORMERS_CACHE,
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TRANSFORMERS_DYNAMIC_MODULE_NAME,
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EntryNotFoundError,
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PushInProgress,
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PushToHubMixin,
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RepositoryNotFoundError,
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RevisionNotFoundError,
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cached_file,
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default_cache_path,
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define_sagemaker_information,
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download_url,
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extract_commit_hash,
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get_cached_models,
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get_file_from_repo,
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has_file,
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http_user_agent,
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is_offline_mode,
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is_remote_url,
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move_cache,
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send_example_telemetry,
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try_to_load_from_cache,
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)
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from .import_utils import (
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ACCELERATE_MIN_VERSION,
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ENV_VARS_TRUE_AND_AUTO_VALUES,
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ENV_VARS_TRUE_VALUES,
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TORCH_FX_REQUIRED_VERSION,
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USE_JAX,
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USE_TF,
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USE_TORCH,
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XLA_FSDPV2_MIN_VERSION,
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DummyObject,
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OptionalDependencyNotAvailable,
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_LazyModule,
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ccl_version,
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direct_transformers_import,
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get_torch_version,
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is_accelerate_available,
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is_apex_available,
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is_aqlm_available,
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is_auto_awq_available,
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is_auto_gptq_available,
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is_av_available,
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is_bitsandbytes_available,
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is_bs4_available,
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is_coloredlogs_available,
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is_cv2_available,
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is_cython_available,
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is_datasets_available,
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is_decord_available,
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is_detectron2_available,
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is_essentia_available,
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is_faiss_available,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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is_flax_available,
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is_fsdp_available,
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is_ftfy_available,
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is_g2p_en_available,
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is_galore_torch_available,
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is_in_notebook,
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is_ipex_available,
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is_jieba_available,
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is_jinja_available,
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is_jumanpp_available,
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is_kenlm_available,
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is_keras_nlp_available,
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is_levenshtein_available,
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is_librosa_available,
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is_mlx_available,
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is_natten_available,
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is_ninja_available,
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is_nltk_available,
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is_onnx_available,
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is_openai_available,
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is_optimum_available,
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is_pandas_available,
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is_peft_available,
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is_phonemizer_available,
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is_pretty_midi_available,
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is_protobuf_available,
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is_psutil_available,
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is_py3nvml_available,
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is_pyctcdecode_available,
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is_pytesseract_available,
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is_pytest_available,
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is_pytorch_quantization_available,
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is_quanto_available,
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is_rjieba_available,
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is_sacremoses_available,
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is_safetensors_available,
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is_sagemaker_dp_enabled,
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is_sagemaker_mp_enabled,
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is_scipy_available,
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is_sentencepiece_available,
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is_seqio_available,
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is_sklearn_available,
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is_soundfile_availble,
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is_spacy_available,
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is_speech_available,
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is_sudachi_available,
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is_sudachi_projection_available,
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is_tensorflow_probability_available,
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is_tensorflow_text_available,
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is_tf2onnx_available,
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is_tf_available,
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is_timm_available,
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is_tokenizers_available,
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is_torch_available,
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is_torch_bf16_available,
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is_torch_bf16_available_on_device,
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is_torch_bf16_cpu_available,
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is_torch_bf16_gpu_available,
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is_torch_compile_available,
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is_torch_cuda_available,
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is_torch_fp16_available_on_device,
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is_torch_fx_available,
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is_torch_fx_proxy,
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is_torch_mlu_available,
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is_torch_mps_available,
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is_torch_neuroncore_available,
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is_torch_npu_available,
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is_torch_sdpa_available,
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is_torch_tensorrt_fx_available,
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is_torch_tf32_available,
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is_torch_tpu_available,
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is_torch_xla_available,
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is_torch_xpu_available,
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is_torchaudio_available,
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is_torchdistx_available,
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is_torchdynamo_available,
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is_torchdynamo_compiling,
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is_torchvision_available,
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is_training_run_on_sagemaker,
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is_vision_available,
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requires_backends,
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torch_only_method,
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)
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from .peft_utils import (
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ADAPTER_CONFIG_NAME,
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ADAPTER_SAFE_WEIGHTS_NAME,
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ADAPTER_WEIGHTS_NAME,
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check_peft_version,
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find_adapter_config_file,
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)
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WEIGHTS_NAME = "pytorch_model.bin"
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WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json"
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TF2_WEIGHTS_NAME = "tf_model.h5"
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TF2_WEIGHTS_INDEX_NAME = "tf_model.h5.index.json"
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TF_WEIGHTS_NAME = "model.ckpt"
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FLAX_WEIGHTS_NAME = "flax_model.msgpack"
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FLAX_WEIGHTS_INDEX_NAME = "flax_model.msgpack.index.json"
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SAFE_WEIGHTS_NAME = "model.safetensors"
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SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json"
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CONFIG_NAME = "config.json"
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FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
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IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME
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PROCESSOR_NAME = "processor_config.json"
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GENERATION_CONFIG_NAME = "generation_config.json"
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MODEL_CARD_NAME = "modelcard.json"
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SENTENCEPIECE_UNDERLINE = "▁"
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SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
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MULTIPLE_CHOICE_DUMMY_INPUTS = [
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[[0, 1, 0, 1], [1, 0, 0, 1]]
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] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
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DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
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DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
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def check_min_version(min_version):
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if version.parse(__version__) < version.parse(min_version):
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if "dev" in min_version:
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error_message = (
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"This example requires a source install from HuggingFace Transformers (see "
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"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
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)
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else:
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error_message = f"This example requires a minimum version of {min_version},"
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error_message += f" but the version found is {__version__}.\n"
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raise ImportError(
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error_message
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+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
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"versions of HuggingFace Transformers."
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)
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