



"""simple docstring"""
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      =				True
except (ImportError, AttributeError):
						__a      =				object
						def    A_  (			*_lowercase,					**_lowercase	):







												'''simple docstring'''


												pass

						__a      =				False


__a      =				logging.get_logger("transformers-cli/serving")
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :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(_lowercase,					args.host,					args.port,					args.workers	)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			dict

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			List[str]
					_A		:			Optional[List[int]]

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			str

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Any

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					@staticmethod
					def    lowerCAmelCase_				(       snake_case:							ArgumentParser						)   ->	Tuple:
											snake_case_ :Any						=			parser.add_parser(
											    """serve"""						,     help="""CLI tool to run inference requests through REST and GraphQL endpoints."""						)
											serve_parser.add_argument(
											    """--task"""						,     type=snake_case						,     choices=get_supported_tasks()						,     help="""The task to run the pipeline on"""						,     )
											serve_parser.add_argument("""--host"""						,     type=snake_case						,     default="""localhost"""						,     help="""Interface the server will listen on."""						)
											serve_parser.add_argument("""--port"""						,     type=snake_case						,     default=8_888						,     help="""Port the serving will listen to."""						)
											serve_parser.add_argument("""--workers"""						,     type=snake_case						,     default=1						,     help="""Number of http workers"""						)
											serve_parser.add_argument("""--model"""						,     type=snake_case						,     help="""Model's name or path to stored model."""						)
											serve_parser.add_argument("""--config"""						,     type=snake_case						,     help="""Model's config name or path to stored model."""						)
											serve_parser.add_argument("""--tokenizer"""						,     type=snake_case						,     help="""Tokenizer name to use."""						)
											serve_parser.add_argument(
											    """--device"""						,     type=snake_case						,     default=-1						,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""						,     )
											serve_parser.set_defaults(func=snake_case						)






					def __init__(       self:							int						,     snake_case:							Pipeline						,     snake_case:							str						,     snake_case:							int						,     snake_case:							int						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			pipeline

											snake_case_ :Optional[Any]						=			host
											snake_case_ :Optional[Any]						=			port
											snake_case_ :Tuple						=			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}"""						)
																	snake_case_ :List[str]						=			FastAPI(
																	    routes=[
																	        APIRoute(
																	            """/"""						,     self.model_info						,     response_model=snake_case						,     response_class=snake_case						,     methods=["""GET"""]						,     ),
																	        APIRoute(
																	            """/tokenize"""						,     self.tokenize						,     response_model=snake_case						,     response_class=snake_case						,     methods=["""POST"""]						,     ),
																	        APIRoute(
																	            """/detokenize"""						,     self.detokenize						,     response_model=snake_case						,     response_class=snake_case						,     methods=["""POST"""]						,     ),
																	        APIRoute(
																	            """/forward"""						,     self.forward						,     response_model=snake_case						,     response_class=snake_case						,     methods=["""POST"""]						,     ),
																	    ]						,     timeout=600						,     )






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Dict:
											run(self._app						,     host=self.host						,     port=self.port						,     workers=self.workers						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	Any:
											return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							str = Body(snake_case						,     embed=snake_case						)						,     snake_case:							bool = Body(snake_case						,     embed=snake_case						)						)   ->	Union[str, Any]:
											try:
																	snake_case_ :Dict						=			self._pipeline.tokenizer.tokenize(snake_case						)

																	if return_ids:
																							snake_case_ :int						=			self._pipeline.tokenizer.convert_tokens_to_ids(snake_case						)
																							return ServeTokenizeResult(tokens=snake_case						,     tokens_ids=snake_case						)
																	else:
																							return ServeTokenizeResult(tokens=snake_case						)

											except Exception as e:
																	raise HTTPException(status_code=500						,     detail={"""model""": """""", """error""": str(snake_case						)}						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							List[int] = Body(snake_case						,     embed=snake_case						)						,     snake_case:							bool = Body(snake_case						,     embed=snake_case						)						,     snake_case:							bool = Body(snake_case						,     embed=snake_case						)						,     )   ->	Union[str, Any]:
											try:
																	snake_case_ :Dict						=			self._pipeline.tokenizer.decode(snake_case						,     snake_case						,     snake_case						)
																	return ServeDeTokenizeResult(model=""""""						,     text=snake_case						)
											except Exception as e:
																	raise HTTPException(status_code=500						,     detail={"""model""": """""", """error""": str(snake_case						)}						)






					async def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Optional[int]=Body(snake_case						,     embed=snake_case						)						)   ->	Union[str, Any]:

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

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





"""simple docstring"""
from __future__ import annotations

from collections import Counter
from random import random
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Tuple						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			{}






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						)   ->	None:
											snake_case_ :str						=			{}






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						,     snake_case:							str						,     snake_case:							float						)   ->	None:
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											if nodea not in self.connections:
																	self.add_node(snake_case						)
											snake_case_ :Dict						=			probability






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	list[str]:
											return list(self.connections						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							str						)   ->	str:
											snake_case_ :Optional[Any]						=			0
											snake_case_ :List[str]						=			random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(_lowercase,					_lowercase,					_lowercase	)

						snake_case_ :int						=			Counter(graph.get_nodes()	)
						snake_case_ :Optional[Any]						=			start

						for _ in range(_lowercase	):
												snake_case_ :Tuple						=			graph.transition(_lowercase	)
												visited[node] += 1

						return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
