






"""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        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  return EnvironmentCommand()






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]					)       ->							Union[str, Any]:







  '''simple docstring'''







  return EnvironmentCommand(args.accelerate_config_file					)




class    lowercase_    (lowercase__     ):






      @staticmethod
      def 			lowerCamelCase__    (    a_	)	->List[str]:







        '''simple docstring'''


        _a							=	parser.add_parser("env"	)
        download_parser.set_defaults(func=__lowerCamelCase	)
        download_parser.add_argument(
            "--accelerate-config_file"     ,     default=__lowerCamelCase     ,     help="The accelerate config file to use for the default values in the launching script."     ,     )
        download_parser.set_defaults(func=__lowerCamelCase	)






      def __init__(    self     ,     a_     ,     *a_	)	->List[Any]:







        '''simple docstring'''


        _a							=	accelerate_config_file






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	"not installed"
        if is_safetensors_available():
          import safetensors

          _a							=	safetensors.__version__
        elif importlib.util.find_spec("safetensors"	) is not None:
          import safetensors

          _a							=	f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

        _a							=	"not installed"
        _a							=	"not found"
        if is_accelerate_available():
          import accelerate
          from accelerate.commands.config import default_config_file, load_config_from_file

          _a							=	accelerate.__version__
          # Get the default from the config file.
          if self._accelerate_config_file is not None or os.path.isfile(__lowerCamelCase	):
            _a							=	load_config_from_file(self._accelerate_config_file	).to_dict()

          _a							=	(
              "\n".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
              if isinstance(__lowerCamelCase     ,     __lowerCamelCase	)
              else f'''\t{accelerate_config}'''
          )

        _a							=	"not installed"
        _a							=	"NA"
        if is_torch_available():
          import torch

          _a							=	torch.__version__
          _a							=	torch.cuda.is_available()

        _a							=	"not installed"
        _a							=	"NA"
        if is_tf_available():
          import tensorflow as tf

          _a							=	tf.__version__
          try:
            # deprecated in v2.1
            _a							=	tf.test.is_gpu_available()
          except AttributeError:
            # returns list of devices, convert to bool
            _a							=	bool(tf.config.list_physical_devices("GPU"	)	)

        _a							=	"not installed"
        _a							=	"not installed"
        _a							=	"not installed"
        _a							=	"NA"
        if is_flax_available():
          import flax
          import jax
          import jaxlib

          _a							=	flax.__version__
          _a							=	jax.__version__
          _a							=	jaxlib.__version__
          _a							=	jax.lib.xla_bridge.get_backend().platform

        _a							=	{
            "`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(__lowerCamelCase	)	)

        return info






      @staticmethod
      def 			lowerCamelCase__    (    a_	)	->Optional[Any]:







        '''simple docstring'''


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










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase							       =    {
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase							       =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


