

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		=True
except (ImportError, AttributeError):
					a		=object






					def        SCREAMING_SNAKE_CASE__	(  *lowerCamelCase__     ,			**lowerCamelCase__      )  ->					Tuple:
						pass

					a		=False


a		=logging.get_logger("""transformers-cli/serving""")






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    List[str] 	=			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_		(						lowerCAmelCase_    ):
							_UpperCAmelCase   :  dict






class 	A_		(						lowerCAmelCase_    ):
							_UpperCAmelCase   :  List[str]
							_UpperCAmelCase   :  Optional[List[int]]






class 	A_		(						lowerCAmelCase_    ):
							_UpperCAmelCase   :  str






class 	A_		(						lowerCAmelCase_    ):
							_UpperCAmelCase   :  Any






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

								__lowerCamelCase    :    Optional[Any] 	=			host
								__lowerCamelCase    :    Dict 	=			port
								__lowerCamelCase    :    Optional[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    :    List[str] 	=			FastAPI(
									    routes=[
									        APIRoute(
									            '/'       ,self.model_info       ,response_model=__SCREAMING_SNAKE_CASE       ,response_class=__SCREAMING_SNAKE_CASE       ,methods=['GET']       ,),
									        APIRoute(
									            '/tokenize'       ,self.tokenize       ,response_model=__SCREAMING_SNAKE_CASE       ,response_class=__SCREAMING_SNAKE_CASE       ,methods=['POST']       ,),
									        APIRoute(
									            '/detokenize'       ,self.detokenize       ,response_model=__SCREAMING_SNAKE_CASE       ,response_class=__SCREAMING_SNAKE_CASE       ,methods=['POST']       ,),
									        APIRoute(
									            '/forward'       ,self.forward       ,response_model=__SCREAMING_SNAKE_CASE       ,response_class=__SCREAMING_SNAKE_CASE       ,methods=['POST']       ,),
									    ]       ,timeout=6_0_0       ,)
							def    lowerCAmelCase					(		self		:      List[str]):
								run(self._app       ,host=self.host       ,port=self.port       ,workers=self.workers)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return ServeModelInfoResult(infos=vars(self._pipeline.model.config))
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      int = Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)):

								try:
									__lowerCamelCase    :    List[str] 	=			self._pipeline.tokenizer.tokenize(__SCREAMING_SNAKE_CASE)

									if return_ids:
										__lowerCamelCase    :    Optional[Any] 	=			self._pipeline.tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)
										return ServeTokenizeResult(tokens=__SCREAMING_SNAKE_CASE       ,tokens_ids=__SCREAMING_SNAKE_CASE)
									else:
										return ServeTokenizeResult(tokens=__SCREAMING_SNAKE_CASE)

								except Exception as e:
									raise HTTPException(status_code=5_0_0       ,detail={'model': '', 'error': str(__SCREAMING_SNAKE_CASE)})
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      int = Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)       ,SCREAMING_SNAKE_CASE__		:      Dict = Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)       ,SCREAMING_SNAKE_CASE__		:      Tuple = Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)       ,):

								try:
									__lowerCamelCase    :    Tuple 	=			self._pipeline.tokenizer.decode(__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE)
									return ServeDeTokenizeResult(model=''       ,text=__SCREAMING_SNAKE_CASE)
								except Exception as e:
									raise HTTPException(status_code=5_0_0       ,detail={'model': '', 'error': str(__SCREAMING_SNAKE_CASE)})


							async def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]=Body(__SCREAMING_SNAKE_CASE       ,embed=__SCREAMING_SNAKE_CASE)):

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




