"""simple docstring"""


import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:
    _snake_case     :  Union[str, Any]     							=							None






@experimental
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  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   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      num_proc if num_proc <= len(lowercase		) else len(lowercase		)
  _UpperCAmelCase						 =      []  # We organize the splits ourselve (contiguous splits)
  for index in range(lowercase		):
    _UpperCAmelCase						 =      len(lowercase		) // num_proc
    _UpperCAmelCase						 =      len(lowercase		) % num_proc
    _UpperCAmelCase						 =      div * index + min(lowercase   ,lowercase		)
    _UpperCAmelCase						 =      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]}'''		)
  _UpperCAmelCase ,				_UpperCAmelCase						 =      None, None
  if not disable_tqdm:
    _UpperCAmelCase ,				_UpperCAmelCase						 =      (RLock(),), tqdm.set_lock
  with Pool(lowercase   ,initargs=lowercase   ,initializer=lowercase		) as pool:
    _UpperCAmelCase						 =      pool.map(lowercase   ,lowercase		)
  logger.info(f'''Finished {num_proc} processes'''		)
  _UpperCAmelCase						 =      [obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(lowercase		)} objects'''		)

  return mapped






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  # 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		):







  """simple docstring"""
  _UpperCAmelCase						 =      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:
    _UpperCAmelCase						 =      None

"""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

    UpperCAmelCase__  		=						True
except (ImportError, AttributeError):
    UpperCAmelCase__  		=						object






    def     __UpperCAmelCase					(			*lowercase   ,**lowercase		):







      """simple docstring"""
      pass

    UpperCAmelCase__  		=						False


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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      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        a				(		lowerCAmelCase_   ):
    _snake_case     :  dict



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  List[str]
    _snake_case     :  Optional[List[int]]



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  str



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Any



class        a				(		lowerCAmelCase_   ):


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      _UpperCAmelCase						 =      parser.add_parser(
          """serve"""   ,				help="""CLI tool to run inference requests through REST and GraphQL endpoints."""					)
      serve_parser.add_argument(
          """--task"""   ,				type=__lowerCAmelCase   ,				choices=get_supported_tasks()   ,				help="""The task to run the pipeline on"""   ,				)
      serve_parser.add_argument("""--host"""   ,				type=__lowerCAmelCase   ,				default="""localhost"""   ,				help="""Interface the server will listen on."""					)
      serve_parser.add_argument("""--port"""   ,				type=__lowerCAmelCase   ,				default=8888   ,				help="""Port the serving will listen to."""					)
      serve_parser.add_argument("""--workers"""   ,				type=__lowerCAmelCase   ,				default=1   ,				help="""Number of http workers"""					)
      serve_parser.add_argument("""--model"""   ,				type=__lowerCAmelCase   ,				help="""Model's name or path to stored model."""					)
      serve_parser.add_argument("""--config"""   ,				type=__lowerCAmelCase   ,				help="""Model's config name or path to stored model."""					)
      serve_parser.add_argument("""--tokenizer"""   ,				type=__lowerCAmelCase   ,				help="""Tokenizer name to use."""					)
      serve_parser.add_argument(
          """--device"""   ,				type=__lowerCAmelCase   ,				default=-1   ,				help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""   ,				)
      serve_parser.set_defaults(func=__lowerCAmelCase					)


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  Pipeline   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      pipeline

      _UpperCAmelCase						 =      host
      _UpperCAmelCase						 =      port
      _UpperCAmelCase						 =      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}'''					)
        _UpperCAmelCase						 =      FastAPI(
            routes=[
                APIRoute(
                    """/"""   ,				self.model_info   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""GET"""]   ,				),
                APIRoute(
                    """/tokenize"""   ,				self.tokenize   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
                APIRoute(
                    """/detokenize"""   ,				self.detokenize   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
                APIRoute(
                    """/forward"""   ,				self.forward   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
            ]   ,				timeout=600   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      run(self._app   ,				host=self.host   ,				port=self.port   ,				workers=self.workers					)


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


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  str = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.tokenize(__lowerCAmelCase					)

        if return_ids:
          _UpperCAmelCase						 =      self._pipeline.tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)
          return ServeTokenizeResult(tokens=__lowerCAmelCase   ,				tokens_ids=__lowerCAmelCase					)
        else:
          return ServeTokenizeResult(tokens=__lowerCAmelCase					)

      except Exception as e:
        raise HTTPException(status_code=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[int] = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.decode(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)
        return ServeDeTokenizeResult(model=""""""   ,				text=__lowerCAmelCase					)
      except Exception as e:
        raise HTTPException(status_code=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    async def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[Any]=Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):

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

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