




'''simple docstring'''





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[str]=() ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   Dict="no" ,						_UpperCamelCase:   Any="29500"   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				False
     _snake_case							=				False
     if any(key.startswith("KAGGLE"   ) for key in os.environ.keys()   ):
          _snake_case							=				True
     elif "IPython" in sys.modules:
          _snake_case							=				"google.colab" in str(sys.modules["IPython"].get_ipython()   )

     try:
          _snake_case							=				PrecisionType(mixed_precision.lower()   )
     except ValueError:
          raise ValueError(
              F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""   )

     if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" ,						_lowerCAmelCase   ) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state   ) > 0:
               raise ValueError(
                   "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                   "your training function. Restart your notebook and make sure no cells initializes an "
                   "`Accelerator`."   )
          if num_processes is None:
               _snake_case							=				8

          _snake_case							=				PrepareForLaunch(_lowerCAmelCase ,						distributed_type="TPU"   )
          print(F"""Launching a training on {num_processes} TPU cores."""   )
          xmp.spawn(_lowerCAmelCase ,						args=_lowerCAmelCase ,						nprocs=_lowerCAmelCase ,						start_method="fork"   )
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print("Launching training on one GPU."   )
          else:
               print("Launching training on one CPU."   )
          function(*_lowerCAmelCase   )
     else:
          if num_processes is None:
               raise ValueError(
                   "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."   )

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state   ) > 0:
                    raise ValueError(
                        "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                        "inside your training function. Restart your notebook and make sure no cells initializes an "
                        "`Accelerator`."   )

               if torch.cuda.is_initialized():
                    raise ValueError(
                        "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                        "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                        "function."   )

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=_lowerCAmelCase ,						master_addr="127.0.01" ,						master_port=_lowerCAmelCase ,						mixed_precision=_lowerCAmelCase   ):
                    _snake_case							=				PrepareForLaunch(_lowerCAmelCase ,						distributed_type="MULTI_GPU"   )
                    print(F"""Launching training on {num_processes} GPUs."""   )
                    try:
                         start_processes(_lowerCAmelCase ,						args=_lowerCAmelCase ,						nprocs=_lowerCAmelCase ,						start_method="fork"   )
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                  "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                  "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                  "which one is problematic."   ) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    _snake_case							=				"1"
                    print("Launching training on MPS."   )
               elif torch.cuda.is_available():
                    print("Launching training on one GPU."   )
               else:
                    print("Launching training on CPU."   )
               function(*_lowerCAmelCase   )





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   int=() ,						_UpperCamelCase:   Dict=2   )						->			int:

     """simple docstring"""




     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=_lowerCAmelCase ,						master_addr="127.0.01" ,						master_port="29500" ,						accelerate_mixed_precision="no" ,						accelerate_debug_rdv_file=tmp_file.name ,						accelerate_use_cpu="yes" ,						):
               _snake_case							=				PrepareForLaunch(_lowerCAmelCase ,						debug=_lowerCAmelCase   )
               start_processes(_lowerCAmelCase ,						args=_lowerCAmelCase ,						nprocs=_lowerCAmelCase ,						start_method="fork"   )








'''simple docstring'''





# Copyright 2021 The HuggingFace Inc. 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.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCamelCase_						:      int       =						'''pytorch_model.bin'''
UpperCamelCase_						:      str       =						'''pytorch_model.bin.index.json'''
UpperCamelCase_						:      int       =						'''adapter_config.json'''
UpperCamelCase_						:      str       =						'''adapter_model.bin'''
UpperCamelCase_						:      str       =						'''adapter_model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''tf_model.h5'''
UpperCamelCase_						:      Union[str, Any]       =						'''tf_model.h5.index.json'''
UpperCamelCase_						:      Tuple       =						'''model.ckpt'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack'''
UpperCamelCase_						:      Union[str, Any]       =						'''flax_model.msgpack.index.json'''
UpperCamelCase_						:      Dict       =						'''model.safetensors'''
UpperCamelCase_						:      List[Any]       =						'''model.safetensors.index.json'''
UpperCamelCase_						:      Tuple       =						'''config.json'''
UpperCamelCase_						:      List[str]       =						'''preprocessor_config.json'''
UpperCamelCase_						:      List[Any]       =						FEATURE_EXTRACTOR_NAME
UpperCamelCase_						:      Union[str, Any]       =						'''generation_config.json'''
UpperCamelCase_						:      str       =						'''modelcard.json'''

UpperCamelCase_						:      List[Any]       =						'''▁'''
UpperCamelCase_						:      Tuple       =						SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCamelCase_						:      Any       =						[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCamelCase_						:      Tuple       =						[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCamelCase_						:      str       =						[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




     if version.parse(_UpperCamelCase   ) < version.parse(_UpperCamelCase   ):
          if "dev" in min_version:
               _snake_case							=				(
                   "This example requires a source install from HuggingFace Transformers (see "
                   "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
               )
          else:
               _snake_case							=				F"""This example requires a minimum version of {min_version},"""
          error_message += F""" but the version found is {__version__}.\n"""
          raise ImportError(
              error_message
              + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
              "versions of HuggingFace Transformers."   )


