'''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 			__UpperCamelCase       (   UpperCAmelCase     ):
 return EnvironmentCommand()
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return EnvironmentCommand(args.accelerate_config_file     )





class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase )  ->  List[Any]:
        lowercase__				:    Optional[Any]  =					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	,   __lowerCAmelCase	,   *__lowerCAmelCase )  ->  None:
        lowercase__				:    Any  =					accelerate_config_file

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    Optional[Any]  =					'''not installed'''
        if is_safetensors_available():
         import safetensors

         lowercase__				:    Union[str, Any]  =					safetensors.__version__
        elif importlib.util.find_spec('''safetensors''' ) is not None:
         import safetensors

         lowercase__				:    int  =					F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

        lowercase__				:    List[Any]  =					'''not installed'''
        lowercase__				:    Tuple  =					'''not found'''
        if is_accelerate_available():
         import accelerate
         from accelerate.commands.config import default_config_file, load_config_from_file

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

         lowercase__				:    List[str]  =					(
             '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
             if isinstance(__lowerCAmelCase	,   __lowerCAmelCase )
             else F"""\t{accelerate_config}"""
         )

        lowercase__				:    Union[str, Any]  =					'''not installed'''
        lowercase__				:    Optional[Any]  =					'''NA'''
        if is_torch_available():
         import torch

         lowercase__				:    Tuple  =					torch.__version__
         lowercase__				:    Optional[int]  =					torch.cuda.is_available()

        lowercase__				:    Union[str, Any]  =					'''not installed'''
        lowercase__				:    str  =					'''NA'''
        if is_tf_available():
         import tensorflow as tf

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

        lowercase__				:    Optional[Any]  =					'''not installed'''
        lowercase__				:    Union[str, Any]  =					'''not installed'''
        lowercase__				:    Any  =					'''not installed'''
        lowercase__				:    Dict  =					'''NA'''
        if is_flax_available():
         import flax
         import jax
         import jaxlib

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

        lowercase__				:    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(__lowerCAmelCase ) )

        return info


       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase )  ->  int:
        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


__a: Dict          =   {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

  __a: Dict          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

