
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  :					Tuple													=       True
except (ImportError, AttributeError):
		lowercase  :					int													=       object




		def 	_SCREAMING_SNAKE_CASE					(		*_lowerCamelCase :			int						,  **_lowerCamelCase :			List[Any])					->    str:






									'''simple docstring'''





									pass




		lowercase  :					Any													=       False


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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Namespace)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42
	_A								=     42




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@staticmethod
	def    _lowerCamelCase				(					a		:ArgumentParser						)		-> int:
								__UpperCamelCase	:   Tuple			   =					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_8_8_8     ,   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		:Optional[int]     ,   a		:Pipeline     ,   a		:str     ,   a		:int     ,   a		:int						)		-> Optional[int]:
								__UpperCamelCase	:   Tuple			   =					pipeline

								__UpperCamelCase	:   int			   =					host
								__UpperCamelCase	:   List[Any]			   =					port
								__UpperCamelCase	:   str			   =					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}'						)
															__UpperCamelCase	:   Optional[Any]			   =					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_0_0     ,   )







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Optional[int]:
								run(self._app     ,   host=self.host     ,   port=self.port     ,   workers=self.workers						)







	def    _lowerCamelCase				(					self		:str						)		-> List[Any]:
								return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)







	def    _lowerCamelCase				(					self		:int     ,   a		:str = Body(a     ,   embed=a						)     ,   a		:bool = Body(a     ,   embed=a						)						)		-> List[Any]:
								try:
															__UpperCamelCase	:   Optional[int]			   =					self._pipeline.tokenizer.tokenize(a						)

															if return_ids:
																						__UpperCamelCase	:   Optional[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_0_0     ,   detail={"model": "", "error": str(a						)}						)







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:List[int] = Body(a     ,   embed=a						)     ,   a		:bool = Body(a     ,   embed=a						)     ,   a		:bool = Body(a     ,   embed=a						)     ,   )		-> str:
								try:
															__UpperCamelCase	:   Tuple			   =					self._pipeline.tokenizer.decode(a     ,   a     ,   a						)
															return ServeDeTokenizeResult(model=""     ,   text=a						)
								except Exception as e:
															raise HTTPException(status_code=5_0_0     ,   detail={"model": "", "error": str(a						)}						)







	async def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[str]=Body(a     ,   embed=a						)						)		-> Optional[int]:

								# Check we don't have empty string
								if len(a						) == 0:
															return ServeForwardResult(output=[]     ,   attention=[]						)

								try:
															# Forward through the model
															__UpperCamelCase	:   Any			   =					self._pipeline(a						)
															return ServeForwardResult(output=a						)
								except Exception as e:
															raise HTTPException(5_0_0     ,   {"error": str(a						)}						)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :					List[str]													=       {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					Optional[Any]													=       [
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		lowercase  :					Optional[Any]													=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)