





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 				_UpperCAmelCase			(    *A				,							**A     ):




       '''simple docstring'''

       pass




    UpperCamelCase_												=	False


UpperCamelCase_												=	logging.get_logger('transformers-cli/serving')






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=pipeline(
       task=args.task				,							model=args.model if args.model else None				,							config=args.config				,							tokenizer=args.tokenizer				,							device=args.device				,							)
   return ServeCommand(A				,							args.host				,							args.port				,							args.workers     )

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42
 __UpperCamelCase       =      42

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      42

class     snake_case_		(   a							):






 '''simple docstring'''





 @staticmethod
 def  __UpperCAmelCase						(  A_		)	->       Any:
    UpperCAmelCase__						=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_		)	->       str:
    UpperCAmelCase__						=pipeline

    UpperCAmelCase__						=host
    UpperCAmelCase__						=port
    UpperCAmelCase__						=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__						=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		)	->       Union[str, Any]:
    run(self._app,		host=self.host,		port=self.port,		workers=self.workers		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    return ServeModelInfoResult(infos=vars(self._pipeline.model.config		)		)

 def  __UpperCAmelCase						(  self,		A_ = Body(A_,		embed=A_		),		A_ = Body(A_,		embed=A_		)		)	->       Any:
    try:
       UpperCAmelCase__						=self._pipeline.tokenizer.tokenize(A_		)

       if return_ids:
          UpperCAmelCase__						=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_		),		)	->       Optional[int]:
    try:
       UpperCAmelCase__						=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_		)		)	->       int:

    # Check we don't have empty string
    if len(A_		) == 0:
       return ServeForwardResult(output=[],		attention=[]		)

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










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



