
"""simple docstring"""




from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

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


try:
   from fastapi import Body, FastAPI, HTTPException
   from fastapi.routing import APIRoute
   from pydantic import BaseModel
   from starlette.responses import JSONResponse
   from uvicorn import run

   _A			:       Tuple   		=       True
except (ImportError, AttributeError):
   _A			:       List[Any]   		=       object


   def   __magic_name__	(      *__snake_case				:       List[str]     , **__snake_case				:       int  ) ->				str:
          pass
   _A			:       int   		=       False


_A			:       Dict   		=       logging.get_logger("""transformers-cli/serving""")


def   __magic_name__	(      __snake_case				:       Namespace  ) ->				Union[str, Any]:
       lowercase    :				List[Any]       		=							pipeline(
           task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
       return ServeCommand(__snake_case     , args.host     , args.port     , args.workers  )

class 		a__						(      a_						):
   __lowerCAmelCase            =				42


class 		a__						(      a_						):
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42


class 		a__						(      a_						):
   __lowerCAmelCase            =				42


class 		a__						(      a_						):
   __lowerCAmelCase            =				42


class 		a__						(      a_						):
   @staticmethod
   def 			__magic_name__			(			_a				):
          lowercase    :				Optional[Any]       		=							parser.add_parser(
              "serve"	,    help="CLI tool to run inference requests through REST and GraphQL endpoints."				)
          serve_parser.add_argument(
              "--task"	,    type=_a	,    choices=get_supported_tasks()	,    help="The task to run the pipeline on"	,    )
          serve_parser.add_argument("--host"	,    type=_a	,    default="localhost"	,    help="Interface the server will listen on."				)
          serve_parser.add_argument("--port"	,    type=_a	,    default=8_888	,    help="Port the serving will listen to."				)
          serve_parser.add_argument("--workers"	,    type=_a	,    default=1	,    help="Number of http workers"				)
          serve_parser.add_argument("--model"	,    type=_a	,    help="Model's name or path to stored model."				)
          serve_parser.add_argument("--config"	,    type=_a	,    help="Model's config name or path to stored model."				)
          serve_parser.add_argument("--tokenizer"	,    type=_a	,    help="Tokenizer name to use."				)
          serve_parser.add_argument(
              "--device"	,    type=_a	,    default=-1	,    help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"	,    )
          serve_parser.set_defaults(func=_a				)
   def __init__(			self	,    _a	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							pipeline

          lowercase    :				Optional[int]       		=							host
          lowercase    :				str       		=							port
          lowercase    :				Optional[int]       		=							workers

          if not _serve_dependencies_installed:
                 raise RuntimeError(
                     "Using serve command requires FastAPI and uvicorn. "
                     "Please install transformers with [serving]: pip install \"transformers[serving]\"."
                     "Or install FastAPI and uvicorn separately."				)
          else:
                 logger.info(f"""Serving model over {host}:{port}"""				)
                 lowercase    :				Any       		=							FastAPI(
                     routes=[
                         APIRoute(
                             "/"	,    self.model_info	,    response_model=_a	,    response_class=_a	,    methods=["GET"]	,    ),
                         APIRoute(
                             "/tokenize"	,    self.tokenize	,    response_model=_a	,    response_class=_a	,    methods=["POST"]	,    ),
                         APIRoute(
                             "/detokenize"	,    self.detokenize	,    response_model=_a	,    response_class=_a	,    methods=["POST"]	,    ),
                         APIRoute(
                             "/forward"	,    self.forward	,    response_model=_a	,    response_class=_a	,    methods=["POST"]	,    ),
                     ]	,    timeout=600	,    )
   def 			__magic_name__			(			self				):
          run(self._app	,    host=self.host	,    port=self.port	,    workers=self.workers				)
   def 			__magic_name__			(			self				):
          return ServeModelInfoResult(infos=vars(self._pipeline.model.config				)				)
   def 			__magic_name__			(			self	,    _a = Body(_a	,    embed=_a				)	,    _a = Body(_a	,    embed=_a				)				):
          try:
                 lowercase    :				Tuple       		=							self._pipeline.tokenizer.tokenize(_a				)

                 if return_ids:
                        lowercase    :				Optional[int]       		=							self._pipeline.tokenizer.convert_tokens_to_ids(_a				)
                        return ServeTokenizeResult(tokens=_a	,    tokens_ids=_a				)
                 else:
                        return ServeTokenizeResult(tokens=_a				)

          except Exception as e:
                 raise HTTPException(status_code=500	,    detail={"model": "", "error": str(_a				)}				)
   def 			__magic_name__			(			self	,    _a = Body(_a	,    embed=_a				)	,    _a = Body(_a	,    embed=_a				)	,    _a = Body(_a	,    embed=_a				)	,    ):
          try:
                 lowercase    :				str       		=							self._pipeline.tokenizer.decode(_a	,    _a	,    _a				)
                 return ServeDeTokenizeResult(model=""	,    text=_a				)
          except Exception as e:
                 raise HTTPException(status_code=500	,    detail={"model": "", "error": str(_a				)}				)

   async def 			__magic_name__			(			self	,    _a=Body(_a	,    embed=_a				)				):

          # Check we don't have empty string
          if len(_a				) == 0:
                 return ServeForwardResult(output=[]	,    attention=[]				)

          try:
                 # Forward through the model
                 lowercase    :				Dict       		=							self._pipeline(_a				)
                 return ServeForwardResult(output=_a				)
          except Exception as e:
                 raise HTTPException(500	,    {"error": str(_a				)}				)




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


