"""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       =					True
except (ImportError, AttributeError):
       __A       =					object



       def SCREAMING_SNAKE_CASE__     (  *__UpperCAmelCase			,       **__UpperCAmelCase					)    ->    int:
          pass




       __A       =					False


__A       =					logging.get_logger("transformers-cli/serving")



def SCREAMING_SNAKE_CASE__     (  __UpperCAmelCase					)    ->    Optional[Any]:
   lowercase__:  Dict   = pipeline(
       task=args.task			,       model=args.model if args.model else None			,       config=args.config			,       tokenizer=args.tokenizer			,       device=args.device			,       )
   return ServeCommand(lowercase_			,       args.host			,       args.port			,       args.workers					)


class        UpperCAmelCase		(_UpperCAmelCase    ):



    """simple docstring"""

    _UpperCAmelCase      :dict



class        UpperCAmelCase		(_UpperCAmelCase    ):



    """simple docstring"""

    _UpperCAmelCase      :List[str]
    _UpperCAmelCase      :Optional[List[int]]



class        UpperCAmelCase		(_UpperCAmelCase    ):



    """simple docstring"""

    _UpperCAmelCase      :str



class        UpperCAmelCase		(_UpperCAmelCase    ):



    """simple docstring"""

    _UpperCAmelCase      :Any



class        UpperCAmelCase		(_UpperCAmelCase    ):



    """simple docstring"""

    @staticmethod
    def _snake_case     (  _UpperCAmelCase				):
       lowercase__:  Union[str, 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=8888       ,     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       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase				):
       lowercase__:  Tuple   = pipeline

       lowercase__:  List[Any]   = host
       lowercase__:  int   = port
       lowercase__:  Tuple   = 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__:  int   = 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 _snake_case     (  self				):
       run(self._app       ,     host=self.host       ,     port=self.port       ,     workers=self.workers				)






    def _snake_case     (  self				):
       return ServeModelInfoResult(infos=vars(self._pipeline.model.config				)				)






    def _snake_case     (  self       ,     _UpperCAmelCase = Body(A__       ,     embed=A__				)       ,     _UpperCAmelCase = Body(A__       ,     embed=A__				)				):
       try:
          lowercase__:  Tuple   = self._pipeline.tokenizer.tokenize(A__				)

          if return_ids:
             lowercase__:  List[Any]   = 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 _snake_case     (  self       ,     _UpperCAmelCase = Body(A__       ,     embed=A__				)       ,     _UpperCAmelCase = Body(A__       ,     embed=A__				)       ,     _UpperCAmelCase = Body(A__       ,     embed=A__				)       ,     ):
       try:
          lowercase__:  Union[str, Any]   = 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 _snake_case     (  self       ,     _UpperCAmelCase=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__:  str   = self._pipeline(A__				)
          return ServeForwardResult(output=A__				)
       except Exception as e:
          raise HTTPException(500       ,     {'''error''': str(A__				)}				)



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



@experimental
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Any       , lowercase_:					str       , lowercase_:					str       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Union[str, Any]   )	-> Union[str, Any]:
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	return _map_with_joblib(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[Any]       , lowercase_:					Tuple       , lowercase_:					Tuple       , lowercase_:					List[str]       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Tuple:
	A__				:       Union[str, Any]	  =						num_proc if num_proc <= len(lowercase_   ) else len(lowercase_   )
	A__				:       str	  =						[]  # We organize the splits ourselve (contiguous splits)
	for index in range(lowercase_   ):
		A__				:       Tuple	  =						len(lowercase_   ) // num_proc
		A__				:       Tuple	  =						len(lowercase_   ) % num_proc
		A__				:       Optional[Any]	  =						div * index + min(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

	if len(lowercase_   ) != sum(len(i[1]   ) for i in split_kwds   ):
		raise ValueError(
		    f"""Error dividing inputs iterable among processes. """
		    f"""Total number of objects {len(lowercase_   )}, """
		    f"""length: {sum(len(i[1]   ) for i in split_kwds   )}"""   )

	logger.info(
	    f"""Spawning {num_proc} processes for {len(lowercase_   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}"""   )
	A__  ,  A__				:       Optional[int]	  =						None, None
	if not disable_tqdm:
		A__  ,  A__				:       List[str]	  =						(RLock(),), tqdm.set_lock
	with Pool(lowercase_       , initargs=lowercase_       , initializer=lowercase_   ) as pool:
		A__				:       Optional[Any]	  =						pool.map(lowercase_       , lowercase_   )
	logger.info(f"""Finished {num_proc} processes"""   )
	A__				:       Tuple	  =						[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"""Unpacked {len(lowercase_   )} objects"""   )

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
	# and it requires monkey-patching joblib internal classes which is subject to change
	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name       , n_jobs=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		A__				:       Optional[Any]	  =						None



