



"""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

	__snake_case							:     List[str]   =					True
except (ImportError, AttributeError):
	__snake_case							:     List[Any]   =					object


	def     _lowercase       (						*__snake_case      ,**__snake_case			)		->     Optional[int]:
				pass





	__snake_case							:     Optional[Any]   =					False


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


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						str				= 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     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			42






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			42
	SCREAMING_SNAKE_CASE					   =			42






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			42






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			42






class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= 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=8888 ,    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:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Pipeline ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	int)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= pipeline

				__lowerCAmelCase   :						str				= host
				__lowerCAmelCase   :						List[Any]				= port
				__lowerCAmelCase   :						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}""")
							__lowerCAmelCase   :						int				= 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=600 ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	str = Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE:	bool = Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE))       -> Dict:

				"""simple docstring"""

				try:
							__lowerCAmelCase   :						int				= self._pipeline.tokenizer.tokenize(_SCREAMING_SNAKE_CASE)

							if return_ids:
										__lowerCAmelCase   :						List[str]				= 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=500 ,    detail={"model": "", "error": str(_SCREAMING_SNAKE_CASE)})

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[int] = Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE:	bool = Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE:	bool = Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE) ,    )       -> List[Any]:

				"""simple docstring"""

				try:
							__lowerCAmelCase   :						Optional[int]				= 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=500 ,    detail={"model": "", "error": str(_SCREAMING_SNAKE_CASE)})







	async def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Any=Body(_SCREAMING_SNAKE_CASE ,    embed=_SCREAMING_SNAKE_CASE))       -> int:

				"""simple docstring"""

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

				try:
							# Forward through the model
							__lowerCAmelCase   :						Dict				= self._pipeline(_SCREAMING_SNAKE_CASE)
							return ServeForwardResult(output=_SCREAMING_SNAKE_CASE)
				except Exception as e:
							raise HTTPException(500 ,    {"error": str(_SCREAMING_SNAKE_CASE)})




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)