
'''simple docstring'''




from argparse import ArgumentParser

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


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







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    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 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  str   =		pipeline(
        task=args.task						,							model=args.model if args.model else None						,							config=args.config						,							tokenizer=args.tokenizer						,							device=args.device						,							)
    A			:  List[str]   =		try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
    A			:  Tuple   =		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							(				__snake_case		):





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							List[Any]:






         """simple docstring"""






         A			:  List[str]   =		nlp
         A			:  str   =		reader





     @staticmethod
     def 							__lowerCAmelCase				(				SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  Tuple   =		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=SCREAMING_SNAKE_CASE		,     help='''Path to the file to use for inference'''      )
         run_parser.add_argument('''--output'''		,     type=SCREAMING_SNAKE_CASE		,     help='''Path to the file that will be used post to write results.'''      )
         run_parser.add_argument('''--model'''		,     type=SCREAMING_SNAKE_CASE		,     help='''Name or path to the model to instantiate.'''      )
         run_parser.add_argument('''--config'''		,     type=SCREAMING_SNAKE_CASE		,     help='''Name or path to the model\'s config to instantiate.'''      )
         run_parser.add_argument(
             '''--tokenizer'''		,     type=SCREAMING_SNAKE_CASE		,     help='''Name of the tokenizer to use. (default: same as the model name)'''      )
         run_parser.add_argument(
             '''--column'''		,     type=SCREAMING_SNAKE_CASE		,     help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''		,     )
         run_parser.add_argument(
             '''--format'''		,     type=SCREAMING_SNAKE_CASE		,     default='''infer'''		,     choices=PipelineDataFormat.SUPPORTED_FORMATS		,     help='''Input format to read from'''		,     )
         run_parser.add_argument(
             '''--device'''		,     type=SCREAMING_SNAKE_CASE		,     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=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A,    A			:  Optional[int]   =		self._nlp, []

         for entry in self._reader:
             A			:  Any   =		nlp(**SCREAMING_SNAKE_CASE      ) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE      )
             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 outputs.append(SCREAMING_SNAKE_CASE      )
             else:
                 outputs += output

        # Saving data
         if self._nlp.binary_output:
             A			:  Optional[int]   =		self._reader.save_binary(SCREAMING_SNAKE_CASE      )
             logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}'      )
         else:
             self._reader.save(SCREAMING_SNAKE_CASE      )






'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase     :							Tuple       						=     '\nHuman: <<task>>\n\nAssistant: '


lowercase     :							Union[str, Any]       						=     'huggingface-tools/default-prompts'
lowercase     :							int       						=     {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__="run"		):




    '''simple docstring'''



    if prompt_or_repo_id is None:
        A			:  List[Any]   =		DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search('''\\s'''						,							snake_case__		) is not None:
        return prompt_or_repo_id

    A			:  int   =		cached_file(
        snake_case__						,							PROMPT_FILES[mode]						,							repo_type='''dataset'''						,							user_agent={'''agent''': agent_name}		)
    with open(snake_case__						,							'''r'''						,							encoding='''utf-8'''		) as f:
        return f.read()




