
# 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


lowerCamelCase_						: Optional[Any]         =  """Create a default config file for Accelerate with only a few flags set."""







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







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				UpperCamelCase_:							Optional[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__					(						lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							Union[str, Any]	=						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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






