



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						(      *_UpperCAmelCase							:					Any ,   **_UpperCAmelCase							:					Tuple						):







						'''simple docstring'''





						pass
				__A   			=False


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

def        a						(      _UpperCAmelCase							:					Namespace						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	pipeline(
		    task=args.task ,   model=args.model if args.model else None ,   config=args.config ,   tokenizer=args.tokenizer ,   device=args.device ,   )
		return ServeCommand(_UpperCAmelCase ,   args.host ,   args.port ,   args.workers						)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42
			UpperCamelCase									=       42

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@staticmethod
			def  snake_case__     (   a_							: ArgumentParser ):
					'''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=88_88       ,       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							: List[Any]       ,       a_							: Pipeline       ,       a_							: str       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	pipeline

					__UpperCAmelCase    :     str													=	host
					__UpperCAmelCase    :     List[str]													=	port
					__UpperCAmelCase    :     List[str]													=	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[int]													=	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_00       ,       )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: str       ,       a_							: str = Body(a_       ,       embed=a_ )       ,       a_							: bool = Body(a_       ,       embed=a_ ) ):
					'''simple docstring'''





					try:
							__UpperCAmelCase    :     List[Any]													=	self._pipeline.tokenizer.tokenize(a_ )

							if return_ids:
									__UpperCAmelCase    :     str													=	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_00       ,       detail={'''model''': '''''', '''error''': str(a_ )} )




			def  snake_case__     (   self							: str       ,       a_							: List[int] = Body(a_       ,       embed=a_ )       ,       a_							: bool = Body(a_       ,       embed=a_ )       ,       a_							: bool = 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=5_00       ,       detail={'''model''': '''''', '''error''': str(a_ )} )




			async def  snake_case__     (   self							: Optional[int]       ,       a_							: Optional[Any]=Body(a_       ,       embed=a_ ) ):
					'''simple docstring'''





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

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










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		__UpperCAmelCase    :     Dict													=	time.time()
		# update config with task specific params
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)
		if prefix is None:
				__UpperCAmelCase    :     List[Any]													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		for examples_chunk in tqdm(list(chunks(_UpperCAmelCase ,   _UpperCAmelCase						)						)						):
				__UpperCAmelCase    :     Any													=	[prefix + text for text in examples_chunk]
				__UpperCAmelCase    :     Union[str, Any]													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt''' ,   truncation=_UpperCAmelCase ,   padding='''longest'''						).to(_UpperCAmelCase						)
				__UpperCAmelCase    :     Dict													=	model.generate(
				    input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     List[Any]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				for hypothesis in dec:
						fout.write(hypothesis + '''\n'''						)
						fout.flush()
		fout.close()
		__UpperCAmelCase    :     Dict													=	int(time.time() - start_time						)  # seconds
		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4						)}

def        a						(      ):







		'''simple docstring'''





		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--dump-args''' ,   action='''store_true''' ,   help='''print the custom hparams with the results'''						)
		parser.add_argument(
		    '''--info''' ,   nargs='''?''' ,   type=_UpperCAmelCase ,   const=datetime_now() ,   help=(
		        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
		        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
		    ) ,   )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		if args.reference_path is None and Path(args.score_path						).exists():
				warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   open(args.score_path ,   '''w'''						)						)

		return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)





