



"""simple docstring"""
# Copyright 2022 The HuggingFace 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase=None							) ->						Dict:







  '''simple docstring'''


  if subparsers is not None:
    lowercase_								= subparsers.add_parser("""env"""							)
  else:
    lowercase_								= argparse.ArgumentParser("""Accelerate env command"""							)

  parser.add_argument(
      """--config_file""" ,  default=__lowerCAmelCase ,  help="""The config file to use for the default values in the launching script."""							)

  if subparsers is not None:
    parser.set_defaults(func=__lowerCAmelCase							)
  return parser


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= torch.__version__
  lowercase_								= torch.cuda.is_available()
  lowercase_								= is_xpu_available()
  lowercase_								= is_npu_available()

  lowercase_								= """Not found"""
  # Get the default from the config file.
  if args.config_file is not None or os.path.isfile(__lowerCAmelCase							):
    lowercase_								= load_config_from_file(args.config_file							).to_dict()

  lowercase_								= {
      """`Accelerate` version""": version,
      """Platform""": platform.platform(),
      """Python version""": platform.python_version(),
      """Numpy version""": np.__version__,
      """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''',
      """PyTorch XPU available""": str(__lowerCAmelCase							),
      """PyTorch NPU available""": str(__lowerCAmelCase							),
      """System RAM""": F'''{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB''',
  }
  if pt_cuda_available:
    lowercase_								= torch.cuda.get_device_name()

  print("""\nCopy-and-paste the text below in your GitHub issue\n"""							)
  print("""\n""".join([F'''- {prop}: {val}''' for prop, val in info.items()]							)							)

  print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""							)
  lowercase_								= (
      """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]							)
      if isinstance(__lowerCAmelCase ,  __lowerCAmelCase							)
      else F'''\t{accelerate_config}'''
  )
  print(__lowerCAmelCase							)

  lowercase_								= accelerate_config

  return info


def 			_SCREAMING_SNAKE_CASE						() ->						int:







  '''simple docstring'''


  lowercase_								= env_command_parser()
  lowercase_								= parser.parse_args()
  env_command(__lowerCAmelCase							)
  return 0


if __name__ == "__main__":
       raise SystemExit(main())






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Any			= {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Dict			= ["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		int			= [
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= [
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure)

