


from argparse import ArgumentParser

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


_snake_case					:		List[str]      =    logging.get_logger(__name__)  # pylint: disable=invalid-name







def     lowerCAmelCase_					( __lowerCamelCase     ):
							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(__lowerCamelCase     ):
																					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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       pipeline(
							    task=args.task							,     model=args.model if args.model else None							,     config=args.config							,     tokenizer=args.tokenizer							,     device=args.device							,     )
							__snake_case		:      Tuple										=       try_infer_format_from_ext(args.input     ) if args.format == "infer" else args.format
							__snake_case		:      int										=       PipelineDataFormat.from_str(
							    format=__lowerCamelCase							,     output_path=args.output							,     input_path=args.input							,     column=args.column if args.column else nlp.default_input_names							,     overwrite=args.overwrite							,     )
							return RunCommand(__lowerCamelCase							,     __lowerCamelCase     )

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Pipeline   ,     lowerCamelCase	:  PipelineDataFormat    )     ->   Dict:
														__snake_case		:      Dict										=       nlp
														__snake_case		:      List[str]										=       reader

							@staticmethod
							def    __snake_case (							lowerCamelCase	:  ArgumentParser    )     ->   Optional[int]:
														__snake_case		:      int										=       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=lowerCamelCase   ,     help="Path to the file to use for inference"    )
														run_parser.add_argument("--output"   ,     type=lowerCamelCase   ,     help="Path to the file that will be used post to write results."    )
														run_parser.add_argument("--model"   ,     type=lowerCamelCase   ,     help="Name or path to the model to instantiate."    )
														run_parser.add_argument("--config"   ,     type=lowerCamelCase   ,     help="Name or path to the model's config to instantiate."    )
														run_parser.add_argument(
														    "--tokenizer"   ,     type=lowerCamelCase   ,     help="Name of the tokenizer to use. (default: same as the model name)"    )
														run_parser.add_argument(
														    "--column"   ,     type=lowerCamelCase   ,     help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"   ,     )
														run_parser.add_argument(
														    "--format"   ,     type=lowerCamelCase   ,     default="infer"   ,     choices=PipelineDataFormat.SUPPORTED_FORMATS   ,     help="Input format to read from"   ,     )
														run_parser.add_argument(
														    "--device"   ,     type=lowerCamelCase   ,     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=lowerCamelCase    )






							def    __snake_case (							self	:  Any    )     ->   List[str]:
														__snake_case			,      __snake_case		:      Optional[Any]										=       self._nlp, []

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

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





# 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


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







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       write_basic_config(args.mixed_precision							,     args.save_location     )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'     )

