# 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


_lowercase				:   str   				="Create a default config file for Accelerate with only a few flags set."



def A__	(      lowercase:  int="no",		lowercase:  List[str] = default_json_config_file,		lowercase:  Tuple = False       )					->		Tuple:
       A    :  Optional[Any]								=Path(lowerCamelCase_       )
       path.parent.mkdir(parents=lowerCamelCase_,		exist_ok=lowerCamelCase_       )
       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[Any]								=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    :  Optional[Any]								={
           'compute_environment': 'LOCAL_MACHINE',
           'mixed_precision': mixed_precision,
       }
       if torch.cuda.is_available():
              A    :  str								=torch.cuda.device_count()
              A    :  Dict								=num_gpus
              A    :  List[Any]								=False
              if num_gpus > 1:
                     A    :  Dict								='MULTI_GPU'
              else:
                     A    :  Optional[int]								='NO'
       elif is_xpu_available() and use_xpu:
              A    :  Any								=torch.xpu.device_count()
              A    :  List[str]								=num_xpus
              A    :  int								=False
              if num_xpus > 1:
                     A    :  Optional[Any]								='MULTI_XPU'
              else:
                     A    :  Optional[Any]								='NO'
       elif is_npu_available():
              A    :  Union[str, Any]								=torch.npu.device_count()
              A    :  Dict								=num_npus
              A    :  str								=False
              if num_npus > 1:
                     A    :  List[str]								='MULTI_NPU'
              else:
                     A    :  Union[str, Any]								='NO'
       else:
              A    :  int								=0
              A    :  Dict								=True
              A    :  Optional[int]								=1
              A    :  List[str]								='NO'
       A    :  Tuple								=ClusterConfig(**lowerCamelCase_       )
       config.to_json_file(lowerCamelCase_       )
       return path



def A__	(      lowercase:  Union[str, Any],		lowercase:  List[Any]       )					->		List[Any]:
       A    :  Union[str, Any]								=parser.add_parser('default',		parents=lowerCamelCase_,		help=lowerCamelCase_,		formatter_class=lowerCamelCase_       )
       parser.add_argument(
           '--config_file',		default=lowerCamelCase_,		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=lowerCamelCase_,		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=lowerCamelCase_       )
       return parser



def A__	(      lowercase:  Tuple       )					->		int:
       A    :  List[str]								=write_basic_config(args.mixed_precision,		args.save_location       )
       if config_file:
              print(F'accelerate configuration saved at {config_file}'       )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			: str				   =					{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: int				   =					[
     "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTBigCodeForSequenceClassification",
     "GPTBigCodeForTokenClassification",
     "GPTBigCodeForCausalLM",
     "GPTBigCodeModel",
     "GPTBigCodePreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


