


'''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

   A				:					int    						=			True
except (ImportError, AttributeError):
   A				:					Union[str, Any]    						=			object






   def 				lowerCAmelCase__	(    *lowerCamelCase					:      Optional[int]  ,**lowerCamelCase					:      Optional[int]       ):
          pass


   A				:					Optional[Any]    						=			False


A				:					int    						=			logging.get_logger('''transformers-cli/serving''')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Namespace       ):
       _A					:     Tuple     = pipeline(
           task=args.task  ,model=args.model if args.model else None  ,config=args.config  ,tokenizer=args.tokenizer  ,device=args.device  ,)
       return ServeCommand(_UpperCAmelCase  ,args.host  ,args.port  ,args.workers       )


class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE_	):
    """simple docstring"""
    a													=   42




class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE_	):
    """simple docstring"""
    a													=   42
    a													=   42




class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE_	):
    """simple docstring"""
    a													=   42




class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE_	):
    """simple docstring"""
    a													=   42




class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE_	):
    """simple docstring"""


    @staticmethod
    def        A   (  SCREAMING_SNAKE_CASE      :							ArgumentParser):
           _A					:     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=8888				,							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      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Pipeline				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     List[str]     = pipeline

           _A					:     List[str]     = host
           _A					:     Optional[int]     = port
           _A					:     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}')
                  _A					:     List[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=600				,							)


    def        A   (  self      :							List[str]):
           run(self._app				,							host=self.host				,							port=self.port				,							workers=self.workers)


    def        A   (  self      :							Optional[Any]):
           return ServeModelInfoResult(infos=vars(self._pipeline.model.config))


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str = Body(__a				,							embed=__a)				,							SCREAMING_SNAKE_CASE      :							bool = Body(__a				,							embed=__a)):
           try:
                  _A					:     str     = self._pipeline.tokenizer.tokenize(__a)

                  if return_ids:
                         _A					:     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=500				,							detail={'model': '', 'error': str(__a)})


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[int] = Body(__a				,							embed=__a)				,							SCREAMING_SNAKE_CASE      :							bool = Body(__a				,							embed=__a)				,							SCREAMING_SNAKE_CASE      :							bool = Body(__a				,							embed=__a)				,							):
           try:
                  _A					:     str     = self._pipeline.tokenizer.decode(__a				,							__a				,							__a)
                  return ServeDeTokenizeResult(model=''				,							text=__a)
           except Exception as e:
                  raise HTTPException(status_code=500				,							detail={'model': '', 'error': str(__a)})


    async def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=Body(__a				,							embed=__a)):

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

           try:
                  # Forward through the model
                  _A					:     Optional[Any]     = self._pipeline(__a)
                  return ServeForwardResult(output=__a)
           except Exception as e:
                  raise HTTPException(500				,							{'error': str(__a)})










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be trained."}	)
    a													=   field(
        default="./"    , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path of training dataset."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for training."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for evaluation."}	)
    a													=   field(default=0.1    , metadata={"help": "Value of weight decay."}	)
    a													=   field(
        default=1_0000    , metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
    a													=   field(default=2E-4    , metadata={"help": "Learning rate fo training."}	)
    a													=   field(default="cosine"    , metadata={"help": "Learning rate."}	)
    a													=   field(
        default=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , metadata={"help": "Maximum number of training steps."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size used for evaluation."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Length of sequences to be evaluated."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , metadata={"help": "Sample from the language model's output distribution."}	)
    a													=   field(default=0.2    , metadata={"help": "Sampling temperature used for generation."}	)
    a													=   field(default=256    , metadata={"help": "Maximum number of newly generated tokens."}	)
    a													=   field(default=0    , metadata={"help": "Top-k parameter used for generation."}	)
    a													=   field(default=0.95    , metadata={"help": "Top-p parameter used for nucleus sampling."}	)
    a													=   field(default=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , metadata={"help": "Number of completions to generate for each sample."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="eval_results.json"    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="0"    , metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
    a													=   field(
        default=-1    , metadata={
            "help": (
                "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
                " number corresponds to which GPU device id to run on."
            )
        }    , )




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }    , )
    a													=   field(
        default="transformersbook/codeparrot"    , metadata={"help": "Folder or name of dataset to process."}	)
    a													=   field(
        default="codeparrot-clean"    , metadata={"help": "Folder to save processed processed dataset."}	)
    a													=   field(
        default=10_0000    , metadata={"help": "Number of files to save per JSON output file."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(
        default=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=0.25    , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
    a													=   field(
        default=1.5    , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
    a													=   field(
        default=0.7    , metadata={"help": "Probability for filtering config, test and uncommon files."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}    , )
    a													=   field(
        default=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2"    , metadata={"help": "Base tokenizer to build new tokenizer from."}	)
    a													=   field(
        default="transformersbook/codeparrot-train"    , metadata={"help": "Dataset to train tokenizer on."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(default=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path to the dataset to pretokenize."}	)
    a													=   field(
        default="tokenized-codeparrot-train"    , metadata={"help": "Repo name of the pretokenized data."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2-large"    , metadata={"help": "Configuration to use for model initialization."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Tokenizer attached to model."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of the created model."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






