

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_rembert import RemBertTokenizer
else:
		lowercase			:				List[str]   =     None

lowercase			:				int   =     logging.get_logger(__name__)
lowercase			:				Optional[Any]   =     {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowercase			:				int   =     {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase			:				Optional[Any]   =     {
    "google/rembert": 2_56,
}

lowercase			:				List[Any]   =     "▁"







class 					__lowercase  (						SCREAMING_SNAKE_CASE_				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[int]								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[str]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		int								=	RemBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=False    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="<unk>"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="<pad>"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														# Mask token behave like a normal word, i.e. include the space before it
														A      :					Any      		=					AddedToken(_a    ,						lstrip=_a    ,						rstrip=_a						) if isinstance(_a    ,						_a						) else mask_token

														super().__init__(
														    _a    ,						tokenizer_file=_a    ,						do_lower_case=_a    ,						remove_space=_a    ,						keep_accents=_a    ,						bos_token=_a    ,						eos_token=_a    ,						unk_token=_a    ,						sep_token=_a    ,						pad_token=_a    ,						cls_token=_a    ,						mask_token=_a    ,						**_a    ,						)

														A      :					List[str]      		=					do_lower_case
														A      :					Optional[int]      		=					remove_space
														A      :					Union[str, Any]      		=					keep_accents
														A      :					Union[str, Any]      		=					vocab_file
														A      :					Dict      		=					False if not self.vocab_file else True




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					int      		=					[self.sep_token_id]
														A      :					List[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = False						)		->       List[int]:

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''						)
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is not None:
																					return [1] + ([0] * len(_a						)) + [1] + ([0] * len(_a						)) + [1]
														return [1] + ([0] * len(_a						)) + [1]




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					Any      		=					[self.sep_token_id]
														A      :					Union[str, Any]      		=					[self.cls_token_id]

														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							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''']						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(_a						):
																					copyfile(self.vocab_file    ,						_a						)

														return (out_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  )}  )






