




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				(						*SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				**SCREAMING_SNAKE_CASE_							:    List[Any]					):
										pass


					UpperCamelCase__         =      False


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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Namespace					):
					__lowerCAmelCase           =	pipeline(
					    task=args.task				,				model=args.model if args.model else None				,				config=args.config				,				tokenizer=args.tokenizer				,				device=args.device				,				)
					return ServeCommand(SCREAMING_SNAKE_CASE_				,				args.host				,				args.port				,				args.workers					)
class       a__   ( snake_case__				):
						_a							:   dict






class       a__   ( snake_case__				):
						_a							:   List[str]
						_a							:   Optional[List[int]]






class       a__   ( snake_case__				):
						_a							:   str






class       a__   ( snake_case__				):
						_a							:   Any






class       a__   ( snake_case__				):






						@staticmethod
						def     __SCREAMING_SNAKE_CASE(       _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	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=8_8_8_8		,      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"""



											__lowerCAmelCase           =	pipeline

											__lowerCAmelCase           =	host
											__lowerCAmelCase           =	port
											__lowerCAmelCase           =	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           =	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=6_0_0		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A = Body(_A		,      embed=_A      )		,      _A = Body(_A		,      embed=_A      )      ):




											"""simple docstring"""



											try:
																__lowerCAmelCase           =	self._pipeline.tokenizer.tokenize(_A      )

																if return_ids:
																					__lowerCAmelCase           =	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=5_0_0		,      detail={"model": "", "error": str(_A      )}      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A = Body(_A		,      embed=_A      )		,      _A = Body(_A		,      embed=_A      )		,      _A = Body(_A		,      embed=_A      )		,      ):




											"""simple docstring"""



											try:
																__lowerCAmelCase           =	self._pipeline.tokenizer.decode(_A		,      _A		,      _A      )
																return ServeDeTokenizeResult(model=""		,      text=_A      )
											except Exception as e:
																raise HTTPException(status_code=5_0_0		,      detail={"model": "", "error": str(_A      )}      )






						async def     __SCREAMING_SNAKE_CASE(       self		,      _A=Body(_A		,      embed=_A      )      ):




											"""simple docstring"""



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

											try:
																# Forward through the model
																__lowerCAmelCase           =	self._pipeline(_A      )
																return ServeForwardResult(output=_A      )
											except Exception as e:
																raise HTTPException(5_0_0		,      {"error": str(_A      )}      )












import math







def      _a				(						SCREAMING_SNAKE_CASE_							:    int					):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5				,				int(math.sqrt(SCREAMING_SNAKE_CASE_					) + 1					)				,				6					):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True







def      _a				(						SCREAMING_SNAKE_CASE_							:    int = 1_00_01					):

					try:
										__lowerCAmelCase           =	int(SCREAMING_SNAKE_CASE_					)
					except (TypeError, ValueError):
										raise TypeError("Parameter nth must be int or castable to int."					) from None
					if nth <= 0:
										raise ValueError("Parameter nth must be greater than or equal to one."					)
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	2
					while len(SCREAMING_SNAKE_CASE_					) < nth:
										if is_prime(SCREAMING_SNAKE_CASE_					):
															primes.append(SCREAMING_SNAKE_CASE_					)
															num += 1
										else:
															num += 1
					return primes[len(SCREAMING_SNAKE_CASE_					) - 1]


if __name__ == "__main__":
					print(f'''{solution() = }''')




