






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


snake_case     					=		logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     question_encoder
       SCREAMING_SNAKE_CASE							:   str						    =     generator
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.question_encoder


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any   ):
       if os.path.isfile(UpperCAmelCase_   ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''   )
       os.makedirs(UpperCAmelCase_       ,			exist_ok=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     os.path.join(UpperCAmelCase_       ,			"question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     os.path.join(UpperCAmelCase_       ,			"generator_tokenizer"   )
       self.question_encoder.save_pretrained(UpperCAmelCase_   )
       self.generator.save_pretrained(UpperCAmelCase_   )


     @classmethod
     def 					_A							(					cls							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]       ,			**UpperCAmelCase_							:						List[str]   ):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     kwargs.pop("config"       ,			UpperCAmelCase_   )

       if config is None:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     RagConfig.from_pretrained(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.question_encoder       ,			subfolder="question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   str						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.generator       ,			subfolder="generator_tokenizer"   )
       return cls(question_encoder=UpperCAmelCase_       ,			generator=UpperCAmelCase_   )


     def __call__(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						int       ,			**UpperCAmelCase_							:						List[str]   ):
       return self.current_tokenizer(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]       ,			*UpperCAmelCase_							:						Union[str, Any]       ,			**UpperCAmelCase_							:						Tuple   ):
       return self.generator.batch_decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			*UpperCAmelCase_							:						Optional[int]       ,			**UpperCAmelCase_							:						int   ):
       return self.generator.decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.question_encoder


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.generator






     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Optional[List[str]] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						str = "longest"       ,			UpperCAmelCase_							:						str = None       ,			UpperCAmelCase_							:						bool = True       ,			**UpperCAmelCase_							:						Optional[Any]       ,			):
       warnings.warn(
           "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
           "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
           "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
           "details"       ,			UpperCAmelCase_       ,			)
       if max_length is None:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self(
           UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       if tgt_texts is None:
         return model_inputs
       # Process tgt_texts
       if max_target_length is None:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   str						    =     self(
           text_target=UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   str						    =     labels["input_ids"]
       return model_inputs











'''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 			a_	(				*_lowerCAmelCase			,**_lowerCAmelCase			) -> Union[str, Any]:
     pass


   _UpperCamelCase											=		False


_UpperCamelCase											=		logging.get_logger('transformers-cli/serving')

def 			a_	(				_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			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(_lowerCAmelCase			,args.host			,args.port			,args.workers			)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42
  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  @staticmethod
  def 		_lowercase	(					_a :      ArgumentParser  )   ->   Optional[Any]:
    __lowerCamelCase			:			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 :      int     ,      _a :      Pipeline     ,      _a :      str     ,      _a :      int     ,      _a :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					pipeline

    __lowerCamelCase			:			List[Any]       			=					host
    __lowerCamelCase			:			int       			=					port
    __lowerCamelCase			:			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}'  )
      __lowerCamelCase			:			Optional[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 		_lowercase	(					self :      Optional[Any]  )   ->   List[str]:
    run(self._app     ,      host=self.host     ,      port=self.port     ,      workers=self.workers  )




  def 		_lowercase	(					self :      Tuple  )   ->   int:
    return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )




  def 		_lowercase	(					self :      str     ,      _a :      str = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )  )   ->   Optional[int]:
    try:
      __lowerCamelCase			:			Dict       			=					self._pipeline.tokenizer.tokenize(_a  )

      if return_ids:
        __lowerCamelCase			:			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 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[int] = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      )   ->   List[Any]:
    try:
      __lowerCamelCase			:			Dict       			=					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 		_lowercase	(					self :      Dict     ,      _a :      Any=Body(_a     ,      embed=_a  )  )   ->   Optional[int]:

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

    try:
      # Forward through the model
      __lowerCamelCase			:			Union[str, Any]       			=					self._pipeline(_a  )
      return ServeForwardResult(output=_a  )
    except Exception as e:
      raise HTTPException(500     ,      {'error': str(_a  )}  )






