





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	:					Dict			  =					True
except (ImportError, AttributeError):
    __lowerCamelCase	:					Any			  =					object



    def       A_    (					*_lowerCAmelCase							,       **_lowerCAmelCase					)						->			Union[str, Any]:
     pass


    __lowerCamelCase	:					Optional[Any]			  =					False


__lowerCamelCase	:					Tuple			  =					logging.get_logger("""transformers-cli/serving""")



def       A_    (					_lowerCAmelCase					)						->			Optional[Any]:
 UpperCamelCase     : int	     =       pipeline(
     task=args.task							,       model=args.model if args.model else None							,       config=args.config							,       tokenizer=args.tokenizer							,       device=args.device							,       )
 return ServeCommand(__snake_case							,       args.host							,       args.port							,       args.workers					)



class  A__       (	lowerCamelCase__      ):
      _UpperCAmelCase     :dict



class  A__       (	lowerCamelCase__      ):
      _UpperCAmelCase     :List[str]
      _UpperCAmelCase     :Optional[List[int]]



class  A__       (	lowerCamelCase__      ):
      _UpperCAmelCase     :str



class  A__       (	lowerCamelCase__      ):
      _UpperCAmelCase     :Any



class  A__       (	lowerCamelCase__      ):





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




       UpperCamelCase     : 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	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       pipeline

       UpperCamelCase     : Optional[int]	     =       host
       UpperCamelCase     : Union[str, Any]	     =       port
       UpperCamelCase     : int	     =       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     : 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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_ = Body(A_	,							embed=A_       )	,							A_ = Body(A_	,							embed=A_       )       ):
       '''simple docstring'''




       try:
        UpperCamelCase     : str	     =       self._pipeline.tokenizer.tokenize(A_       )

        if return_ids:
         UpperCamelCase     : Dict	     =       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 	__UpperCamelCase(			self	,							A_ = Body(A_	,							embed=A_       )	,							A_ = Body(A_	,							embed=A_       )	,							A_ = Body(A_	,							embed=A_       )	,							):
       '''simple docstring'''




       try:
        UpperCamelCase     : int	     =       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 	__UpperCamelCase(			self	,							A_=Body(A_	,							embed=A_       )       ):
       '''simple docstring'''




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

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







from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_=None	,							A_=None	,							A_=0       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       1.0 if scale is None else scale
       UpperCamelCase     : Optional[int]	     =       0.0 if loc is None else loc

       super().__init__(A_	,							[AffineTransform(loc=self.loc	,							scale=self.scale	,							event_dim=A_       )]       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.mean * self.scale + self.loc





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.variance * self.scale**2





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.variance.sqrt()



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Union[str, Any]	     =       args_dim
       UpperCamelCase     : str	     =       nn.ModuleList([nn.Linear(A_	,							A_       ) for dim in args_dim.values()]       )
       UpperCamelCase     : Union[str, Any]	     =       domain_map





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       [proj(A_       ) for proj in self.proj]

       return self.domain_map(*A_       )



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_       ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase     : str	     =       function





      def 	__UpperCamelCase(			self	,							A_	,							*A_       ):
       '''simple docstring'''




       return self.function(A_	,							*A_       )



class  A__       :
      _UpperCAmelCase     :type
      _UpperCAmelCase     :int
      _UpperCAmelCase     :Dict[str, int]





      def __init__(			self	,							A_ = 1       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       dim
       UpperCamelCase     : Union[str, Any]	     =       {k: dim * self.args_dim[k] for k in self.args_dim}





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.dim == 1:
        return self.distribution_class(*A_       )
       else:
        return Independent(self.distribution_class(*A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None	,							):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self._base_distribution(A_       )
       if loc is None and scale is None:
        return distr
       else:
        return AffineTransformed(A_	,							loc=A_	,							scale=A_	,							event_dim=self.event_dim       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return () if self.dim == 1 else (self.dim,)





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return len(self.event_shape       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 0.0





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return ParameterProjection(
           in_features=A_	,							args_dim=self.args_dim	,							domain_map=LambdaLayer(self.domain_map       )	,							)





      def 	__UpperCamelCase(			self	,							*A_       ):
       '''simple docstring'''




       raise NotImplementedError()





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




       return (x + torch.sqrt(torch.square(A_       ) + 4.0       )) / 2.0



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"df": 1, "loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       StudentT





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       UpperCamelCase     : int	     =       2.0 + cls.squareplus(A_       )
       return df.squeeze(-1       ), loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       Normal





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       return loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"total_count": 1, "logits": 1}
      _UpperCAmelCase     :type						   =       NegativeBinomial





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       cls.squareplus(A_       )
       return total_count.squeeze(-1       ), logits.squeeze(-1       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       distr_args
       if self.dim == 1:
        return self.distribution_class(total_count=A_	,							logits=A_       )
       else:
        return Independent(self.distribution_class(total_count=A_	,							logits=A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Any	     =       distr_args

       if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

       return self._base_distribution((total_count, logits)       )
