






"""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

			lowerCamelCase_       			=   True
except (ImportError, AttributeError):
			lowerCamelCase_       			=   object

			def        snake_case   (     *A__			,**A__		):
						pass




			lowerCamelCase_       			=   False


lowerCamelCase_       			=   logging.get_logger('''transformers-cli/serving''')

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Any      =      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     UpperCamelCase_ (__A  ):
	__magic_name__			   =       42







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       42
	__magic_name__			   =       42







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       42







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       42







class     UpperCamelCase_ (__A  ):





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				ArgumentParser		)		->    List[Any]:
				UpperCAmelCase_  :				Optional[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=8_888			,			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      :				List[Any]			,			lowerCAmelCase_      :				Pipeline			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int		)		->    Dict:
				UpperCAmelCase_  :				Optional[int]      =      pipeline

				UpperCAmelCase_  :				int      =      host
				UpperCAmelCase_  :				List[str]      =      port
				UpperCAmelCase_  :				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_  :				Dict      =      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=600			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				run(self._app			,			host=self.host			,			port=self.port			,			workers=self.workers		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Union[str, Any]:
				return ServeModelInfoResult(infos=vars(self._pipeline.model.config		)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				str = Body(lowerCAmelCase_			,			embed=lowerCAmelCase_		)			,			lowerCAmelCase_      :				bool = Body(lowerCAmelCase_			,			embed=lowerCAmelCase_		)		)		->    List[Any]:
				try:
							UpperCAmelCase_  :				Optional[Any]      =      self._pipeline.tokenizer.tokenize(lowerCAmelCase_		)

							if return_ids:
										UpperCAmelCase_  :				int      =      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=500			,			detail={"model": "", "error": str(lowerCAmelCase_		)}		)





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





	async def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Any=Body(lowerCAmelCase_			,			embed=lowerCAmelCase_		)		)		->    Optional[Any]:

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

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











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

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			dtype=tf.floataa			,			)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,			expected_slice.numpy()			,			atol=1e-4		)		)



