


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   :					Tuple	=      True
except (ImportError, AttributeError):
    A   :					Union[str, Any]	=      object

    def    lowercase_	(	*_A    :					List[Any]					,      **_A    :					Any		):

          """simple docstring"""




          pass


    A   :					int	=      False


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

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      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     _lowercase				(							_A):
       A__      		=    42







class     _lowercase				(							_A):
       A__      		=    42
       A__      		=    42







class     _lowercase				(							_A):
       A__      		=    42







class     _lowercase				(							_A):
       A__      		=    42







class     _lowercase				(							_A):





       @staticmethod
       def 		lowerCAmelCase  (     __lowerCamelCase							:    ArgumentParser ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						parser.add_parser(
                 "serve"      ,   help="CLI tool to run inference requests through REST and GraphQL endpoints." )
             serve_parser.add_argument(
                 "--task"      ,   type=UpperCamelCase__      ,   choices=get_supported_tasks()      ,   help="The task to run the pipeline on"      ,   )
             serve_parser.add_argument("--host"      ,   type=UpperCamelCase__      ,   default="localhost"      ,   help="Interface the server will listen on." )
             serve_parser.add_argument("--port"      ,   type=UpperCamelCase__      ,   default=8888      ,   help="Port the serving will listen to." )
             serve_parser.add_argument("--workers"      ,   type=UpperCamelCase__      ,   default=1      ,   help="Number of http workers" )
             serve_parser.add_argument("--model"      ,   type=UpperCamelCase__      ,   help="Model's name or path to stored model." )
             serve_parser.add_argument("--config"      ,   type=UpperCamelCase__      ,   help="Model's config name or path to stored model." )
             serve_parser.add_argument("--tokenizer"      ,   type=UpperCamelCase__      ,   help="Tokenizer name to use." )
             serve_parser.add_argument(
                 "--device"      ,   type=UpperCamelCase__      ,   default=-1      ,   help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,   )
             serve_parser.set_defaults(func=UpperCamelCase__ )





       def __init__(     self							:    Optional[Any]      ,   __lowerCamelCase							:    Pipeline      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						pipeline

             lowerCamelCase__	:       List[Any]					=						host
             lowerCamelCase__	:       Union[str, Any]					=						port
             lowerCamelCase__	:       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=UpperCamelCase__      ,   response_class=UpperCamelCase__      ,   methods=["GET"]      ,   ),
                           APIRoute(
                               "/tokenize"      ,   self.tokenize      ,   response_model=UpperCamelCase__      ,   response_class=UpperCamelCase__      ,   methods=["POST"]      ,   ),
                           APIRoute(
                               "/detokenize"      ,   self.detokenize      ,   response_model=UpperCamelCase__      ,   response_class=UpperCamelCase__      ,   methods=["POST"]      ,   ),
                           APIRoute(
                               "/forward"      ,   self.forward      ,   response_model=UpperCamelCase__      ,   response_class=UpperCamelCase__      ,   methods=["POST"]      ,   ),
                       ]      ,   timeout=600      ,   )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    str = Body(UpperCamelCase__      ,   embed=UpperCamelCase__ )      ,   __lowerCamelCase							:    bool = Body(UpperCamelCase__      ,   embed=UpperCamelCase__ ) ):


             '''simple docstring'''



             try:
                   lowerCamelCase__	:       Any					=						self._pipeline.tokenizer.tokenize(UpperCamelCase__ )

                   if return_ids:
                         lowerCamelCase__	:       Union[str, Any]					=						self._pipeline.tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
                         return ServeTokenizeResult(tokens=UpperCamelCase__      ,   tokens_ids=UpperCamelCase__ )
                   else:
                         return ServeTokenizeResult(tokens=UpperCamelCase__ )

             except Exception as e:
                   raise HTTPException(status_code=500      ,   detail={"model": "", "error": str(UpperCamelCase__ )} )





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    List[int] = Body(UpperCamelCase__      ,   embed=UpperCamelCase__ )      ,   __lowerCamelCase							:    bool = Body(UpperCamelCase__      ,   embed=UpperCamelCase__ )      ,   __lowerCamelCase							:    bool = Body(UpperCamelCase__      ,   embed=UpperCamelCase__ )      ,   ):


             '''simple docstring'''



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





       async def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Optional[Any]=Body(UpperCamelCase__      ,   embed=UpperCamelCase__ ) ):


             '''simple docstring'''



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

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






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


