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		:       Union[str, Any]									=     True
except (ImportError, AttributeError):
					__UpperCamelCase		:       Dict									=     object
					def       A	(    *_lowercase		,  **_lowercase						):
											pass



					__UpperCamelCase		:       List[Any]									=     False


__UpperCamelCase		:       List[Any]									=     logging.get_logger('transformers-cli/serving')
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							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 					lowercase__						(   __A):
						UpperCamelCase_     =		42






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






class 					lowercase__						(   __A):
						UpperCamelCase_     =		42






class 					lowercase__						(   __A):
						UpperCamelCase_     =		42






class 					lowercase__						(   __A):


						@staticmethod
						def        __A       (       UpperCamelCase__	:				ArgumentParser						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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=8888    ,		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	:				int    ,		UpperCamelCase__	:				Pipeline    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							pipeline

												SCREAMING_SNAKE_CASE							:    Any							=							host
												SCREAMING_SNAKE_CASE							:    List[str]							=							port
												SCREAMING_SNAKE_CASE							:    List[Any]							=							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}"""						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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=600    ,		)


						def        __A       (       self	:				int						):





												'''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	:				Union[str, Any]    ,		UpperCamelCase__	:				str = Body(lowercase_    ,		embed=lowercase_						)    ,		UpperCamelCase__	:				bool = Body(lowercase_    ,		embed=lowercase_						)						):





												'''simple docstring'''





												try:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self._pipeline.tokenizer.tokenize(lowercase_						)

																		if return_ids:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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=500    ,		detail={'''model''': '''''', '''error''': str(lowercase_						)}						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				List[int] = Body(lowercase_    ,		embed=lowercase_						)    ,		UpperCamelCase__	:				bool = Body(lowercase_    ,		embed=lowercase_						)    ,		UpperCamelCase__	:				bool = Body(lowercase_    ,		embed=lowercase_						)    ,		):





												'''simple docstring'''





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



						async def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				int=Body(lowercase_    ,		embed=lowercase_						)						):





												'''simple docstring'''





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

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






from PIL import Image

def 			lowercase_				(			_lowerCamelCase							:       Image		,	_lowerCamelCase							:       int):
    lowercase__ :   List[str]    					=				(259 * (level + 255)) / (255 * (259 - level))

    def contrast(_lowerCamelCase							:       int) -> int:
        return int(128 + factor * (c - 128))

    return img.point(_lowerCamelCase)


if __name__ == "__main__":
      # Load image
      with Image.open('''image_data/lena.jpg''') as img:
            # Change contrast to 170
            UpperCamelCase  							=						change_contrast(img, 170)
            cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')



