

'''simple docstring'''


import math
import sys

import cva
import numpy as np




def 	_a       (				__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  math.sqrt(__lowerCAmelCase     )
		snake_case__						:       Union[str, Any]       						=  1 / (sigma * math.sqrt(2 * math.pi     ))
		return cons * np.exp(-((img / sigma) ** 2) * 0.5     )




def 	_a       (				__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  kernel_size // 2
		return img[x - half : x + half + 1, y - half : y + half + 1]




def 	_a       (				__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  np.zeros((kernel_size, kernel_size)     )
		for i in range(0   ,							__lowerCAmelCase     ):
				for j in range(0   ,							__lowerCAmelCase     ):
						snake_case__						:       Any       						=  math.sqrt(
						    abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
		return vec_gaussian(__lowerCAmelCase   ,							__lowerCAmelCase     )




def 	_a       (				__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      int   ,							):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  np.zeros(img.shape     )
		snake_case__						:       Optional[Any]       						=  get_gauss_kernel(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  img.shape
		for i in range(kernel_size // 2   ,							size_x - kernel_size // 2     ):
				for j in range(kernel_size // 2   ,							size_y - kernel_size // 2     ):
						snake_case__						:       Tuple       						=  get_slice(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
						snake_case__						:       str       						=  img_s - img_s[kernel_size // 2, kernel_size // 2]
						snake_case__						:       List[Any]       						=  vec_gaussian(__lowerCAmelCase   ,							__lowerCAmelCase     )
						snake_case__						:       Any       						=  np.multiply(__lowerCAmelCase   ,							__lowerCAmelCase     )
						snake_case__						:       List[Any]       						=  np.multiply(__lowerCAmelCase   ,							__lowerCAmelCase     )
						snake_case__						:       Dict       						=  np.sum(__lowerCAmelCase     ) / np.sum(__lowerCAmelCase     )
						snake_case__						:       Any       						=  val
		return imga




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  args[1] if args[1:] else '''../image_data/lena.jpg'''
		snake_case__						:       List[str]       						=  float(args[2]     ) if args[2:] else 1.0
		snake_case__						:       Dict       						=  float(args[3]     ) if args[3:] else 1.0
		if args[4:]:
				snake_case__						:       Union[str, Any]       						=  int(args[4]     )
				snake_case__						:       Any       						=  kernel_size + abs(kernel_size % 2 - 1     )
		else:
				snake_case__						:       Any       						=  5
		return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
		lowerCAmelCase__						:       Any										=						parse_args(sys.argv)
		lowerCAmelCase__						:       str										=						cva.imread(filename, 0)
		cva.imshow("""input image""", img)

		lowerCAmelCase__						:       int										=						img / 255
		lowerCAmelCase__						:       Dict										=						out.astype("""float32""")
		lowerCAmelCase__						:       Any										=						bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
		lowerCAmelCase__						:       str										=						out * 255
		lowerCAmelCase__						:       str										=						np.uinta(out)
		cva.imshow("""output image""", out)
		cva.waitKey(0)
		cva.destroyAllWindows()






'''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__						:       Any										=						True
except (ImportError, AttributeError):
					lowerCAmelCase__						:       Dict										=						object




					def 	_a       (				*__lowerCAmelCase		:      List[str]   ,							**__lowerCAmelCase		:      Tuple     ):

							"""simple docstring"""

							pass





					lowerCAmelCase__						:       str										=						False


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




def 	_a       (				__lowerCAmelCase		:      Namespace     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  pipeline(
		    task=args.task   ,							model=args.model if args.model else None   ,							config=args.config   ,							tokenizer=args.tokenizer   ,							device=args.device   ,							)
		return ServeCommand(__lowerCAmelCase   ,							args.host   ,							args.port   ,							args.workers     )




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@staticmethod
						def       __magic_name__							(							snake_case_					:      ArgumentParser  ):



								'''simple docstring'''



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







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      Pipeline			,			snake_case_					:      str			,			snake_case_					:      int			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  pipeline

								snake_case__						:       Tuple       						=  host
								snake_case__						:       Optional[Any]       						=  port
								snake_case__						:       Tuple       						=  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}"""  )
										snake_case__						:       str       						=  FastAPI(
										    routes=[
										        APIRoute(
										            '''/'''			,			self.model_info			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''GET''']			,			),
										        APIRoute(
										            '''/tokenize'''			,			self.tokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/detokenize'''			,			self.detokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/forward'''			,			self.forward			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										    ]			,			timeout=6_0_0			,			)







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      str = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[Any]       						=  self._pipeline.tokenizer.tokenize(snake_case_  )

										if return_ids:
												snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.convert_tokens_to_ids(snake_case_  )
												return ServeTokenizeResult(tokens=snake_case_			,			tokens_ids=snake_case_  )
										else:
												return ServeTokenizeResult(tokens=snake_case_  )

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







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[int] = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.decode(snake_case_			,			snake_case_			,			snake_case_  )
										return ServeDeTokenizeResult(model=''''''			,			text=snake_case_  )
								except Exception as e:
										raise HTTPException(status_code=5_0_0			,			detail={'''model''': '''''', '''error''': str(snake_case_  )}  )







						async def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]=Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



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

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



