


"""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


a__    :							Union[str, Any]            =					'''Create a default config file for Accelerate with only a few flags set.'''




def        UpperCAmelCase__	(lowerCAmelCase_="no"  ,						lowerCAmelCase_ = default_json_config_file  ,						lowerCAmelCase_ = False       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    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
					__SCREAMING_SNAKE_CASE						       =    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}"""       )
					__SCREAMING_SNAKE_CASE						       =    {
					    "compute_environment": "LOCAL_MACHINE",
					    "mixed_precision": mixed_precision,
					}
					if torch.cuda.is_available():
										__SCREAMING_SNAKE_CASE						       =    torch.cuda.device_count()
										__SCREAMING_SNAKE_CASE						       =    num_gpus
										__SCREAMING_SNAKE_CASE						       =    False
										if num_gpus > 1:
															__SCREAMING_SNAKE_CASE						       =    "MULTI_GPU"
										else:
															__SCREAMING_SNAKE_CASE						       =    "NO"
					elif is_xpu_available() and use_xpu:
										__SCREAMING_SNAKE_CASE						       =    torch.xpu.device_count()
										__SCREAMING_SNAKE_CASE						       =    num_xpus
										__SCREAMING_SNAKE_CASE						       =    False
										if num_xpus > 1:
															__SCREAMING_SNAKE_CASE						       =    "MULTI_XPU"
										else:
															__SCREAMING_SNAKE_CASE						       =    "NO"
					elif is_npu_available():
										__SCREAMING_SNAKE_CASE						       =    torch.npu.device_count()
										__SCREAMING_SNAKE_CASE						       =    num_npus
										__SCREAMING_SNAKE_CASE						       =    False
										if num_npus > 1:
															__SCREAMING_SNAKE_CASE						       =    "MULTI_NPU"
										else:
															__SCREAMING_SNAKE_CASE						       =    "NO"
					else:
										__SCREAMING_SNAKE_CASE						       =    0
										__SCREAMING_SNAKE_CASE						       =    True
										__SCREAMING_SNAKE_CASE						       =    1
										__SCREAMING_SNAKE_CASE						       =    "NO"
					__SCREAMING_SNAKE_CASE						       =    ClusterConfig(**lowerCAmelCase_       )
					config.to_json_file(lowerCAmelCase_       )
					return path




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    write_basic_config(args.mixed_precision  ,						args.save_location       )
					if config_file:
										print(f"""accelerate configuration saved at {config_file}"""       )




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
