


"""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

			SCREAMING_SNAKE_CASE :    str									=				True
except (ImportError, AttributeError):
			SCREAMING_SNAKE_CASE :    Any									=				object






			def 		lowercase							(     *_snake_case						:      Tuple     ,   **_snake_case						:      Optional[Any]     )     ->Union[str, Any]:
				"""simple docstring"""





				pass

			SCREAMING_SNAKE_CASE :    str									=				False


SCREAMING_SNAKE_CASE :    Any									=				logging.get_logger("""transformers-cli/serving""")






def 		lowercase							(     _snake_case						:      Namespace     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						Tuple						=				pipeline(
	    task=args.task     ,   model=args.model if args.model else None     ,   config=args.config     ,   tokenizer=args.tokenizer     ,   device=args.device     ,   )
	return ServeCommand(_snake_case     ,   args.host     ,   args.port     ,   args.workers     )



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=42



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=42
		lowerCamelCase__				=42



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=42



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=42



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		@staticmethod
		def 				SCREAMING_SNAKE_CASE  (a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				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=88_88				,       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_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				pipeline

			__snake_case	:						int						=				host
			__snake_case	:						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	:						Dict						=				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=6_00				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = Body(a_				,       embed=a_					)				,       a_ = Body(a_				,       embed=a_					)					):





			'''simple docstring'''

			try:
				__snake_case	:						int						=				self._pipeline.tokenizer.tokenize(a_					)

				if return_ids:
					__snake_case	:						Union[str, Any]						=				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=5_00				,       detail={'''model''': '''''', '''error''': str(a_					)}					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = Body(a_				,       embed=a_					)				,       a_ = Body(a_				,       embed=a_					)				,       a_ = Body(a_				,       embed=a_					)				,       ):





			'''simple docstring'''

			try:
				__snake_case	:						List[Any]						=				self._pipeline.tokenizer.decode(a_				,       a_				,       a_					)
				return ServeDeTokenizeResult(model=''''''				,       text=a_					)
			except Exception as e:
				raise HTTPException(status_code=5_00				,       detail={'''model''': '''''', '''error''': str(a_					)}					)



		async def 				SCREAMING_SNAKE_CASE  (self				,       a_=Body(a_				,       embed=a_					)					):





			'''simple docstring'''

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

			try:
				# Forward through the model
				__snake_case	:						Dict						=				self._pipeline(a_					)
				return ServeForwardResult(output=a_					)
			except Exception as e:
				raise HTTPException(5_00				,       {'''error''': str(a_					)}					)









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





