



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

							__snake_case       :	Tuple            =True
except (ImportError, AttributeError):
							__snake_case       :	Optional[Any]            =object






							def 			lowerCAmelCase__	(					*lowerCamelCase_	:	Tuple ,**lowerCamelCase_	:	Tuple):





											'''simple docstring'''

											pass

							__snake_case       :	List[Any]            =False


__snake_case       :	List[Any]            =logging.get_logger('transformers-cli/serving')






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Namespace):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  pipeline(
				    task=args.task ,model=args.model if args.model else None ,config=args.config ,tokenizer=args.tokenizer ,device=args.device ,)
				return ServeCommand(lowerCamelCase_ ,args.host ,args.port ,args.workers)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		@staticmethod
		def 	lowerCAmelCase__   (__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  parser.add_parser(
						    '''serve'''	,help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''				)
						serve_parser.add_argument(
						    '''--task'''	,type=__lowerCamelCase	,choices=get_supported_tasks()	,help='''The task to run the pipeline on'''	,)
						serve_parser.add_argument('''--host'''	,type=__lowerCamelCase	,default='''localhost'''	,help='''Interface the server will listen on.'''				)
						serve_parser.add_argument('''--port'''	,type=__lowerCamelCase	,default=88_88	,help='''Port the serving will listen to.'''				)
						serve_parser.add_argument('''--workers'''	,type=__lowerCamelCase	,default=1	,help='''Number of http workers'''				)
						serve_parser.add_argument('''--model'''	,type=__lowerCamelCase	,help='''Model\'s name or path to stored model.'''				)
						serve_parser.add_argument('''--config'''	,type=__lowerCamelCase	,help='''Model\'s config name or path to stored model.'''				)
						serve_parser.add_argument('''--tokenizer'''	,type=__lowerCamelCase	,help='''Tokenizer name to use.'''				)
						serve_parser.add_argument(
						    '''--device'''	,type=__lowerCamelCase	,default=-1	,help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''	,)
						serve_parser.set_defaults(func=__lowerCamelCase				)



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  pipeline

						lowerCAmelCase__  :       int				=  host
						lowerCAmelCase__  :       Optional[Any]				=  port
						lowerCAmelCase__  :       Optional[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}"""				)
										lowerCAmelCase__  :       Optional[int]				=  FastAPI(
										    routes=[
										        APIRoute(
										            '''/'''	,self.model_info	,response_model=__lowerCamelCase	,response_class=__lowerCamelCase	,methods=['''GET''']	,),
										        APIRoute(
										            '''/tokenize'''	,self.tokenize	,response_model=__lowerCamelCase	,response_class=__lowerCamelCase	,methods=['''POST''']	,),
										        APIRoute(
										            '''/detokenize'''	,self.detokenize	,response_model=__lowerCamelCase	,response_class=__lowerCamelCase	,methods=['''POST''']	,),
										        APIRoute(
										            '''/forward'''	,self.forward	,response_model=__lowerCamelCase	,response_class=__lowerCamelCase	,methods=['''POST''']	,),
										    ]	,timeout=6_00	,)



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

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase = Body(__lowerCamelCase	,embed=__lowerCamelCase				)	,__lowerCamelCase = Body(__lowerCamelCase	,embed=__lowerCamelCase				)				)  ->    Any:

						"""simple docstring"""






						try:
										lowerCAmelCase__  :       Tuple				=  self._pipeline.tokenizer.tokenize(__lowerCamelCase				)

										if return_ids:
														lowerCAmelCase__  :       Tuple				=  self._pipeline.tokenizer.convert_tokens_to_ids(__lowerCamelCase				)
														return ServeTokenizeResult(tokens=__lowerCamelCase	,tokens_ids=__lowerCamelCase				)
										else:
														return ServeTokenizeResult(tokens=__lowerCamelCase				)

						except Exception as e:
										raise HTTPException(status_code=5_00	,detail={'''model''': '''''', '''error''': str(__lowerCamelCase				)}				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase = Body(__lowerCamelCase	,embed=__lowerCamelCase				)	,__lowerCamelCase = Body(__lowerCamelCase	,embed=__lowerCamelCase				)	,__lowerCamelCase = Body(__lowerCamelCase	,embed=__lowerCamelCase				)	,)  ->    List[str]:

						"""simple docstring"""






						try:
										lowerCAmelCase__  :       List[str]				=  self._pipeline.tokenizer.decode(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
										return ServeDeTokenizeResult(model=''''''	,text=__lowerCamelCase				)
						except Exception as e:
										raise HTTPException(status_code=5_00	,detail={'''model''': '''''', '''error''': str(__lowerCamelCase				)}				)



		async def 	lowerCAmelCase__   (self	,__lowerCamelCase=Body(__lowerCamelCase	,embed=__lowerCamelCase				)				)  ->    Optional[Any]:

						"""simple docstring"""






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

						try:
										# Forward through the model
										lowerCAmelCase__  :       int				=  self._pipeline(__lowerCamelCase				)
										return ServeForwardResult(output=__lowerCamelCase				)
						except Exception as e:
										raise HTTPException(5_00	,{'''error''': str(__lowerCamelCase				)}				)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

