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


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


def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			int    )					->							Optional[Any]:
       lowercase       :							List[Any]									=				parser.add_parser("default"	,					parents=__snake_case	,					help=__snake_case	,					formatter_class=__snake_case    )
       parser.add_argument(
           "--config_file"	,					default=__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=__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=__snake_case    )
       return parser


def     __magic_name__      ( __snake_case							:			Any    )					->							Optional[int]:
       lowercase       :							Tuple									=				write_basic_config(args.mixed_precision	,					args.save_location    )
       if config_file:
              print(f"""accelerate configuration saved at {config_file}"""    )




"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


