

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_	:			Dict  	=      True
except (ImportError, AttributeError):
 A_	:			Optional[Any]  	=      object

 def 						UpperCamelCase (*lowercase_:			Optional[int]   ,				**lowercase_:			Tuple )					->				Any:
  pass


 A_	:			int  	=      False


A_	:			Optional[int]  	=      logging.get_logger('transformers-cli/serving')

def 						UpperCamelCase (lowercase_:			str )					->				List[Any]:
 A__							:   Tuple					       =		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    _a						(__lowercase    ):


     '''simple docstring'''






     UpperCAmelCase__:       dict



class    _a						(__lowercase    ):


     '''simple docstring'''






     UpperCAmelCase__:       List[str]
     UpperCAmelCase__:       Optional[List[int]]



class    _a						(__lowercase    ):


     '''simple docstring'''






     UpperCAmelCase__:       str



class    _a						(__lowercase    ):


     '''simple docstring'''






     UpperCAmelCase__:       Any



class    _a						(__lowercase    ):


     '''simple docstring'''






     @staticmethod
     def   __A      (			A__      ):
      A__							:   int					       =		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   ,	A__   ,	A__   ,	A__   ,	A__      ):
      A__							:   Dict					       =		pipeline

      A__							:   str					       =		host
      A__							:   List[Any]					       =		port
      A__							:   Any					       =		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}"""      )
       A__							:   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   __A      (			self      ):
      run(self._app   ,	host=self.host   ,	port=self.port   ,	workers=self.workers      )






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






     def   __A      (			self   ,	A__ = Body(A__   ,	embed=A__      )   ,	A__ = Body(A__   ,	embed=A__      )      ):

      try:
       A__							:   Union[str, Any]					       =		self._pipeline.tokenizer.tokenize(A__      )

       if return_ids:
        A__							:   Tuple					       =		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   __A      (			self   ,	A__ = Body(A__   ,	embed=A__      )   ,	A__ = Body(A__   ,	embed=A__      )   ,	A__ = Body(A__   ,	embed=A__      )   ,	):

      try:
       A__							:   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   __A      (			self   ,	A__=Body(A__   ,	embed=A__      )      ):

      if len(A__      ) == 0:
       return ServeForwardResult(output=[]   ,	attention=[]      )

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






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



