






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

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

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



def     __lowercase					(    a :				int )					->					str:
      if version.parse(a ) < version.parse(a ):
            if "dev" in min_version:
                  __snake_case      :    Union[str, Any]  			=(
                      '''This example requires a source install from HuggingFace Transformers (see '''
                      '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
                  )
            else:
                  __snake_case      :    List[Any]  			=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.''' )










"""simple docstring"""





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def     __lowercase					(    a :				Dict )					->					str:
      return EnvironmentCommand()



def     __lowercase					(    a :				Optional[int] )					->					List[Any]:
      return EnvironmentCommand(args.accelerate_config_file )


class      _lowercase (     lowerCAmelCase						):


      @staticmethod
      def _UpperCamelCase						(    a				:	ArgumentParser						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=parser.add_parser('''env'''						)
            download_parser.set_defaults(func=a						)
            download_parser.add_argument(
                '''--accelerate-config_file'''							,   default=a							,   help='''The accelerate config file to use for the default values in the launching script.'''							,   )
            download_parser.set_defaults(func=a						)


      def __init__(    self				:	Any							,   a				:	str							,   *a				:	Any						):





            """simple docstring"""







            __snake_case      :    int  			=accelerate_config_file


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    List[str]  			='''not installed'''
            if is_safetensors_available():
                  import safetensors

                  __snake_case      :    int  			=safetensors.__version__
            elif importlib.util.find_spec('''safetensors'''						) is not None:
                  import safetensors

                  __snake_case      :    List[Any]  			=f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

            __snake_case      :    Dict  			='''not installed'''
            __snake_case      :    Optional[Any]  			='''not found'''
            if is_accelerate_available():
                  import accelerate
                  from accelerate.commands.config import default_config_file, load_config_from_file

                  __snake_case      :    Union[str, Any]  			=accelerate.__version__
                  # Get the default from the config file.
                  if self._accelerate_config_file is not None or os.path.isfile(a						):
                        __snake_case      :    int  			=load_config_from_file(self._accelerate_config_file						).to_dict()

                  __snake_case      :    Optional[int]  			=(
                      '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]						)
                      if isinstance(a							,   a						)
                      else f'''\t{accelerate_config}'''
                  )

            __snake_case      :    int  			='''not installed'''
            __snake_case      :    Any  			='''NA'''
            if is_torch_available():
                  import torch

                  __snake_case      :    Tuple  			=torch.__version__
                  __snake_case      :    Optional[Any]  			=torch.cuda.is_available()

            __snake_case      :    Optional[Any]  			='''not installed'''
            __snake_case      :    Any  			='''NA'''
            if is_tf_available():
                  import tensorflow as tf

                  __snake_case      :    Optional[Any]  			=tf.__version__
                  try:
                        # deprecated in v2.1
                        __snake_case      :    str  			=tf.test.is_gpu_available()
                  except AttributeError:
                        # returns list of devices, convert to bool
                        __snake_case      :    List[Any]  			=bool(tf.config.list_physical_devices('''GPU'''						)						)

            __snake_case      :    Optional[int]  			='''not installed'''
            __snake_case      :    Dict  			='''not installed'''
            __snake_case      :    List[str]  			='''not installed'''
            __snake_case      :    Optional[int]  			='''NA'''
            if is_flax_available():
                  import flax
                  import jax
                  import jaxlib

                  __snake_case      :    Tuple  			=flax.__version__
                  __snake_case      :    Optional[Any]  			=jax.__version__
                  __snake_case      :    str  			=jaxlib.__version__
                  __snake_case      :    Tuple  			=jax.lib.xla_bridge.get_backend().platform

            __snake_case      :    List[str]  			={
                '''`transformers` version''': version,
                '''Platform''': platform.platform(),
                '''Python version''': platform.python_version(),
                '''Huggingface_hub version''': huggingface_hub.__version__,
                '''Safetensors version''': f'''{safetensors_version}''',
                '''Accelerate version''': f'''{accelerate_version}''',
                '''Accelerate config''': f'''{accelerate_config_str}''',
                '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
                '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
                '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
                '''Jax version''': f'''{jax_version}''',
                '''JaxLib version''': f'''{jaxlib_version}''',
                '''Using GPU in script?''': '''<fill in>''',
                '''Using distributed or parallel set-up in script?''': '''<fill in>''',
            }

            print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''						)
            print(self.format_dict(a						)						)

            return info







      @staticmethod
      def _UpperCamelCase						(    a				:	List[str]						):





            """simple docstring"""







            return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"


