

"""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

							__UpperCamelCase    : int				 =						True
except (ImportError, AttributeError):
							__UpperCamelCase    : List[str]				 =						object

							def     __SCREAMING_SNAKE_CASE    ( *A_		,	**A_					):
								pass





							__UpperCamelCase    : str				 =						False


__UpperCamelCase    : Union[str, Any]				 =						logging.get_logger('''transformers-cli/serving''')

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Tuple    							=      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 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							ArgumentParser						):
						lowerCAmelCase__      :		Dict    							=      parser.add_parser(
						    '''serve'''  ,help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''						)
						serve_parser.add_argument(
						    '''--task'''  ,type=lowercase_  ,choices=get_supported_tasks()  ,help='''The task to run the pipeline on'''  ,)
						serve_parser.add_argument('''--host'''  ,type=lowercase_  ,default='''localhost'''  ,help='''Interface the server will listen on.'''						)
						serve_parser.add_argument('''--port'''  ,type=lowercase_  ,default=8_8_8_8  ,help='''Port the serving will listen to.'''						)
						serve_parser.add_argument('''--workers'''  ,type=lowercase_  ,default=1  ,help='''Number of http workers'''						)
						serve_parser.add_argument('''--model'''  ,type=lowercase_  ,help='''Model\'s name or path to stored model.'''						)
						serve_parser.add_argument('''--config'''  ,type=lowercase_  ,help='''Model\'s config name or path to stored model.'''						)
						serve_parser.add_argument('''--tokenizer'''  ,type=lowercase_  ,help='''Tokenizer name to use.'''						)
						serve_parser.add_argument(
						    '''--device'''  ,type=lowercase_  ,default=-1  ,help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''  ,)
						serve_parser.set_defaults(func=lowercase_						)







					def __init__(			self     :							Dict  ,lowercase_     :							Pipeline  ,lowercase_     :							str  ,lowercase_     :							int  ,lowercase_     :							int						):
						lowerCAmelCase__      :		int    							=      pipeline

						lowerCAmelCase__      :		Dict    							=      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=lowercase_  ,response_class=lowercase_  ,methods=['''GET''']  ,),
							        APIRoute(
							            '''/tokenize'''  ,self.tokenize  ,response_model=lowercase_  ,response_class=lowercase_  ,methods=['''POST''']  ,),
							        APIRoute(
							            '''/detokenize'''  ,self.detokenize  ,response_model=lowercase_  ,response_class=lowercase_  ,methods=['''POST''']  ,),
							        APIRoute(
							            '''/forward'''  ,self.forward  ,response_model=lowercase_  ,response_class=lowercase_  ,methods=['''POST''']  ,),
							    ]  ,timeout=6_0_0  ,)







					def 			__lowerCAmelCase					(			self     :							int						):
						run(self._app  ,host=self.host  ,port=self.port  ,workers=self.workers						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str = Body(lowercase_  ,embed=lowercase_						)  ,lowercase_     :							bool = Body(lowercase_  ,embed=lowercase_						)						):
						try:
							lowerCAmelCase__      :		Union[str, Any]    							=      self._pipeline.tokenizer.tokenize(lowercase_						)

							if return_ids:
								lowerCAmelCase__      :		List[str]    							=      self._pipeline.tokenizer.convert_tokens_to_ids(lowercase_						)
								return ServeTokenizeResult(tokens=lowercase_  ,tokens_ids=lowercase_						)
							else:
								return ServeTokenizeResult(tokens=lowercase_						)

						except Exception as e:
							raise HTTPException(status_code=5_0_0  ,detail={'''model''': '''''', '''error''': str(lowercase_						)}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int] = Body(lowercase_  ,embed=lowercase_						)  ,lowercase_     :							bool = Body(lowercase_  ,embed=lowercase_						)  ,lowercase_     :							bool = Body(lowercase_  ,embed=lowercase_						)  ,):
						try:
							lowerCAmelCase__      :		Optional[int]    							=      self._pipeline.tokenizer.decode(lowercase_  ,lowercase_  ,lowercase_						)
							return ServeDeTokenizeResult(model=''''''  ,text=lowercase_						)
						except Exception as e:
							raise HTTPException(status_code=5_0_0  ,detail={'''model''': '''''', '''error''': str(lowercase_						)}						)







					async def 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							str=Body(lowercase_  ,embed=lowercase_						)						):

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

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







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




