



"""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_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
		return EnvironmentCommand()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
		return EnvironmentCommand(args.accelerate_config_file	)




class 			snake_case_						(						a_				):






							@staticmethod
							def snake_case_  (   a_      ):
									a_     :      Any 						=     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			,     a_			,     *a_      ):
									a_     :      int 						=     accelerate_config_file






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "not installed"
									if is_safetensors_available():
											import safetensors

											a_     :      Optional[Any] 						=     safetensors.__version__
									elif importlib.util.find_spec("safetensors"      ) is not None:
											import safetensors

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

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

											a_     :      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_      ):
													a_     :      Optional[int] 						=     load_config_from_file(self._accelerate_config_file      ).to_dict()

											a_     :      Optional[Any] 						=     (
											    "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]      )
											    if isinstance(a_			,     a_      )
											    else F"""\t{accelerate_config}"""
											)

									a_     :      Union[str, Any] 						=     "not installed"
									a_     :      Union[str, Any] 						=     "NA"
									if is_torch_available():
											import torch

											a_     :      List[Any] 						=     torch.__version__
											a_     :      List[str] 						=     torch.cuda.is_available()

									a_     :      int 						=     "not installed"
									a_     :      List[Any] 						=     "NA"
									if is_tf_available():
											import tensorflow as tf

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

									a_     :      Dict 						=     "not installed"
									a_     :      List[str] 						=     "not installed"
									a_     :      int 						=     "not installed"
									a_     :      Dict 						=     "NA"
									if is_flax_available():
											import flax
											import jax
											import jaxlib

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

									a_     :      Union[str, Any] 						=     {
									    "`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 snake_case_  (   a_      ):
									return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]      ) + "\n"




"""simple docstring"""
# Copyright 2021 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)