



'''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

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




  def lowerCAmelCase    (*__A    ,      **__A):



       """simple docstring"""



       pass
  lowercase_									=    False


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




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			dict






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[str]
 __lowerCamelCase       :			Optional[List[int]]






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			str






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Any






class __A     (    A		):



 '''simple docstring'''







 @staticmethod
 def    a__    (A			)       ->	Any:


      """simple docstring"""



      _a           =    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=8_888					,  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					,  A					,  A					,  A					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    pipeline

      _a           =    host
      _a           =    port
      _a           =    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}'''			)
           _a           =    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=600					,  )



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


      """simple docstring"""



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



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



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



 def    a__    (self					,  A = Body(A					,  embed=A			)					,  A = Body(A					,  embed=A			)			)       ->	str:


      """simple docstring"""



      try:
           _a           =    self._pipeline.tokenizer.tokenize(A			)

           if return_ids:
                _a           =    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=500					,  detail={'''model''': '''''', '''error''': str(A			)}			)



 def    a__    (self					,  A = Body(A					,  embed=A			)					,  A = Body(A					,  embed=A			)					,  A = Body(A					,  embed=A			)					,  )       ->	List[str]:


      """simple docstring"""



      try:
           _a           =    self._pipeline.tokenizer.decode(A					,  A					,  A			)
           return ServeDeTokenizeResult(model=''''''					,  text=A			)
      except Exception as e:
           raise HTTPException(status_code=500					,  detail={'''model''': '''''', '''error''': str(A			)}			)



 async def    a__    (self					,  A=Body(A					,  embed=A			)			)       ->	List[str]:


      """simple docstring"""



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

      try:
           # Forward through the model
           _a           =    self._pipeline(A			)
           return ServeForwardResult(output=A			)
      except Exception as e:
           raise HTTPException(500					,  {'''error''': str(A			)}			)











'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"      )

       # using the PNDM scheduler by default
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image      ).max() < 1e-2




