
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

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


    def 							SCREAMING_SNAKE_CASE_      (			*UpperCamelCase__ ,		**UpperCamelCase__		):
          pass
    lowerCamelCase      =False


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Optional[Any]					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      return ServeCommand(UpperCamelCase__ ,		args.host ,		args.port ,		args.workers		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42
     SCREAMING_SNAKE_CASE_		   =  42




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				parser.add_parser(
               '''serve'''						,						help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''  )
           serve_parser.add_argument(
               '''--task'''						,						type=__SCREAMING_SNAKE_CASE						,						choices=get_supported_tasks()						,						help='''The task to run the pipeline on'''						,						)
           serve_parser.add_argument('''--host'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''localhost'''						,						help='''Interface the server will listen on.'''  )
           serve_parser.add_argument('''--port'''						,						type=__SCREAMING_SNAKE_CASE						,						default=8_8_8_8						,						help='''Port the serving will listen to.'''  )
           serve_parser.add_argument('''--workers'''						,						type=__SCREAMING_SNAKE_CASE						,						default=1						,						help='''Number of http workers'''  )
           serve_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Model\'s name or path to stored model.'''  )
           serve_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Model\'s config name or path to stored model.'''  )
           serve_parser.add_argument('''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Tokenizer name to use.'''  )
           serve_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           serve_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				pipeline

           UpperCamelCase__    :   int					=				host
           UpperCamelCase__    :   Dict					=				port
           UpperCamelCase__    :   Union[str, 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}'''  )
                 UpperCamelCase__    :   List[str]					=				FastAPI(
                     routes=[
                         APIRoute(
                             '''/'''						,						self.model_info						,						response_model=__SCREAMING_SNAKE_CASE						,						response_class=__SCREAMING_SNAKE_CASE						,						methods=['''GET''']						,						),
                         APIRoute(
                             '''/tokenize'''						,						self.tokenize						,						response_model=__SCREAMING_SNAKE_CASE						,						response_class=__SCREAMING_SNAKE_CASE						,						methods=['''POST''']						,						),
                         APIRoute(
                             '''/detokenize'''						,						self.detokenize						,						response_model=__SCREAMING_SNAKE_CASE						,						response_class=__SCREAMING_SNAKE_CASE						,						methods=['''POST''']						,						),
                         APIRoute(
                             '''/forward'''						,						self.forward						,						response_model=__SCREAMING_SNAKE_CASE						,						response_class=__SCREAMING_SNAKE_CASE						,						methods=['''POST''']						,						),
                     ]						,						timeout=6_0_0						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           run(self._app						,						host=self.host						,						port=self.port						,						workers=self.workers  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE = Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )						,						__SCREAMING_SNAKE_CASE = Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )  )  ->   Optional[int]:




           """simple docstring"""







           try:
                 UpperCamelCase__    :   Optional[Any]					=				self._pipeline.tokenizer.tokenize(__SCREAMING_SNAKE_CASE  )

                 if return_ids:
                       UpperCamelCase__    :   Dict					=				self._pipeline.tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
                       return ServeTokenizeResult(tokens=__SCREAMING_SNAKE_CASE						,						tokens_ids=__SCREAMING_SNAKE_CASE  )
                 else:
                       return ServeTokenizeResult(tokens=__SCREAMING_SNAKE_CASE  )

           except Exception as e:
                 raise HTTPException(status_code=5_0_0						,						detail={'''model''': '''''', '''error''': str(__SCREAMING_SNAKE_CASE  )}  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE = Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )						,						__SCREAMING_SNAKE_CASE = Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )						,						__SCREAMING_SNAKE_CASE = Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )						,						)  ->   Union[str, Any]:




           """simple docstring"""







           try:
                 UpperCamelCase__    :   Optional[Any]					=				self._pipeline.tokenizer.decode(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 return ServeDeTokenizeResult(model=''''''						,						text=__SCREAMING_SNAKE_CASE  )
           except Exception as e:
                 raise HTTPException(status_code=5_0_0						,						detail={'''model''': '''''', '''error''': str(__SCREAMING_SNAKE_CASE  )}  )






     async def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=Body(__SCREAMING_SNAKE_CASE						,						embed=__SCREAMING_SNAKE_CASE  )  )  ->   List[Any]:




           """simple docstring"""







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

           try:
                 # Forward through the model
                 UpperCamelCase__    :   Optional[int]					=				self._pipeline(__SCREAMING_SNAKE_CASE  )
                 return ServeForwardResult(output=__SCREAMING_SNAKE_CASE  )
           except Exception as e:
                 raise HTTPException(5_0_0						,						{'''error''': str(__SCREAMING_SNAKE_CASE  )}  )




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  return ext

      raise Exception(
          f'''Unable to determine file format from file extension {path}. '''
          f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				parser.add_parser('''run'''						,						help='''Run a pipeline through the CLI'''  )
           run_parser.add_argument('''--task'''						,						choices=get_supported_tasks()						,						help='''Task to run'''  )
           run_parser.add_argument('''--input'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           run_parser.add_argument('''--overwrite'''						,						action='''store_true'''						,						help='''Allow overwriting the output file.'''  )
           run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

        # Saving data
           if self._nlp.binary_output:
                 UpperCamelCase__    :   Union[str, Any]					=				self._reader.save_binary(__SCREAMING_SNAKE_CASE  )
                 logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''  )
           else:
                 self._reader.save(__SCREAMING_SNAKE_CASE  )


