

import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Dict   =     {"vocab_file": "vocab.json"}

lowercase			:				Optional[Any]   =     {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowercase			:				str   =     {"mgp-str": 27}







class 					__lowercase  (						SCREAMING_SNAKE_CASE_				):





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		str								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase="[GO]"    ,						__UpperCAmelCase="[GO]"    ,						__UpperCAmelCase="[s]"    ,						__UpperCAmelCase="[GO]"    ,						**__UpperCAmelCase						)		->       Optional[int]:
														super().__init__(
														    unk_token=_a    ,						bos_token=_a    ,						eos_token=_a    ,						pad_token=_a    ,						**_a    ,						)

														with open(_a    ,						encoding='''utf-8'''						) as vocab_handle:
																					A      :					List[str]      		=					json.load(_a						)
														A      :					Tuple      		=					{v: k for k, v in self.vocab.items()}




							@property
							def       snake_case			(					self						)		->       List[str]:
														return len(self.vocab						)




							def       snake_case			(					self						)		->       Tuple:
														return dict(self.vocab    ,						**self.added_tokens_encoder						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       int:
														A      :					Optional[Any]      		=					[]
														for s in text:
																					char_tokens.extend(_a						)
														return char_tokens




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Dict:
														return self.vocab.get(_a    ,						self.vocab.get(self.unk_token						)						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														return self.decoder.get(_a						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														if not os.path.isdir(_a						):
																					logger.error('''Vocabulary path ({}) should be a directory'''.format(_a						)						)
																					return
														A      :					List[Any]      		=					os.path.join(
														    _a    ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

														with open(_a    ,						'''w'''    ,						encoding='''utf-8'''						) as f:
																					f.write(json.dumps(self.vocab    ,						indent=2    ,						sort_keys=_a    ,						ensure_ascii=_a						) + '''\n'''						)

														return (vocab_file,)











'''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  )}  )






