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

   __lowerCamelCase			: List[Any]							   =		True
except (ImportError, AttributeError):
   __lowerCamelCase			: Optional[int]							   =		object



   def 	__SCREAMING_SNAKE_CASE  (					*__UpperCamelCase    :       Dict ,     **__UpperCamelCase    :       Dict	)							->					Optional[int]:







     """simple docstring"""





     pass






   __lowerCamelCase			: Optional[Any]							   =		False


__lowerCamelCase			: Tuple							   =		logging.get_logger('''transformers-cli/serving''')



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Namespace	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      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 __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							42







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							42







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							42







class __snake_case				(						lowerCamelCase_		):




   @staticmethod
   def 	__a						(    _lowercase			:				ArgumentParser      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parser.add_parser(
         """serve"""       ,	help="""CLI tool to run inference requests through REST and GraphQL endpoints."""      )
     serve_parser.add_argument(
         """--task"""       ,	type=_lowercase       ,	choices=get_supported_tasks()       ,	help="""The task to run the pipeline on"""       ,	)
     serve_parser.add_argument("""--host"""       ,	type=_lowercase       ,	default="""localhost"""       ,	help="""Interface the server will listen on."""      )
     serve_parser.add_argument("""--port"""       ,	type=_lowercase       ,	default=88_88       ,	help="""Port the serving will listen to."""      )
     serve_parser.add_argument("""--workers"""       ,	type=_lowercase       ,	default=1       ,	help="""Number of http workers"""      )
     serve_parser.add_argument("""--model"""       ,	type=_lowercase       ,	help="""Model's name or path to stored model."""      )
     serve_parser.add_argument("""--config"""       ,	type=_lowercase       ,	help="""Model's config name or path to stored model."""      )
     serve_parser.add_argument("""--tokenizer"""       ,	type=_lowercase       ,	help="""Tokenizer name to use."""      )
     serve_parser.add_argument(
         """--device"""       ,	type=_lowercase       ,	default=-1       ,	help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""       ,	)
     serve_parser.set_defaults(func=_lowercase      )




   def __init__(    self			:				Dict       ,	_lowercase			:				Pipeline       ,	_lowercase			:				str       ,	_lowercase			:				int       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      pipeline

     SCREAMING_SNAKE_CASE__										=      host
     SCREAMING_SNAKE_CASE__										=      port
     SCREAMING_SNAKE_CASE__										=      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}"""      )
       SCREAMING_SNAKE_CASE__										=      FastAPI(
           routes=[
               APIRoute(
                   """/"""       ,	self.model_info       ,	response_model=_lowercase       ,	response_class=_lowercase       ,	methods=["""GET"""]       ,	),
               APIRoute(
                   """/tokenize"""       ,	self.tokenize       ,	response_model=_lowercase       ,	response_class=_lowercase       ,	methods=["""POST"""]       ,	),
               APIRoute(
                   """/detokenize"""       ,	self.detokenize       ,	response_model=_lowercase       ,	response_class=_lowercase       ,	methods=["""POST"""]       ,	),
               APIRoute(
                   """/forward"""       ,	self.forward       ,	response_model=_lowercase       ,	response_class=_lowercase       ,	methods=["""POST"""]       ,	),
           ]       ,	timeout=6_00       ,	)




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int       ,	_lowercase			:				str = Body(_lowercase       ,	embed=_lowercase      )       ,	_lowercase			:				bool = Body(_lowercase       ,	embed=_lowercase      )      ):



     """simple docstring"""






     try:
       SCREAMING_SNAKE_CASE__										=      self._pipeline.tokenizer.tokenize(_lowercase      )

       if return_ids:
         SCREAMING_SNAKE_CASE__										=      self._pipeline.tokenizer.convert_tokens_to_ids(_lowercase      )
         return ServeTokenizeResult(tokens=_lowercase       ,	tokens_ids=_lowercase      )
       else:
         return ServeTokenizeResult(tokens=_lowercase      )

     except Exception as e:
       raise HTTPException(status_code=5_00       ,	detail={"""model""": """""", """error""": str(_lowercase      )}      )




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				List[int] = Body(_lowercase       ,	embed=_lowercase      )       ,	_lowercase			:				bool = Body(_lowercase       ,	embed=_lowercase      )       ,	_lowercase			:				bool = Body(_lowercase       ,	embed=_lowercase      )       ,	):



     """simple docstring"""






     try:
       SCREAMING_SNAKE_CASE__										=      self._pipeline.tokenizer.decode(_lowercase       ,	_lowercase       ,	_lowercase      )
       return ServeDeTokenizeResult(model=""""""       ,	text=_lowercase      )
     except Exception as e:
       raise HTTPException(status_code=5_00       ,	detail={"""model""": """""", """error""": str(_lowercase      )}      )




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



     """simple docstring"""






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

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

import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCamelCase			: Any							   =		16
__lowerCamelCase			: List[Any]							   =		32



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int = 16 ,     __UpperCamelCase    :       str = "bert-base-cased"	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      load_dataset("""glue""" ,     """mrpc"""	)

  def tokenize_function(__UpperCamelCase    :       Optional[Any]	):
    # max_length=None => use the model max length (it's actually the default)
    SCREAMING_SNAKE_CASE__										=      tokenizer(examples["""sentence1"""] ,     examples["""sentence2"""] ,     truncation=__UpperCamelCase ,     max_length=__UpperCamelCase	)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  SCREAMING_SNAKE_CASE__										=      datasets.map(
      __UpperCamelCase ,     batched=__UpperCamelCase ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=__UpperCamelCase	)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  SCREAMING_SNAKE_CASE__										=      tokenized_datasets.rename_column("""label""" ,     """labels"""	)

  def collate_fn(__UpperCamelCase    :       List[Any]	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__UpperCamelCase ,     padding="""max_length""" ,     max_length=1_28 ,     return_tensors="""pt"""	)
    return tokenizer.pad(__UpperCamelCase ,     padding="""longest""" ,     return_tensors="""pt"""	)

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""train"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""validation"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)

  return train_dataloader, eval_dataloader



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__										=      0
  for step, batch in enumerate(__UpperCamelCase	):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device	)
    with torch.no_grad():
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      outputs.logits.argmax(dim=-1	)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.gather(
        (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(__UpperCamelCase	) - 1:
        SCREAMING_SNAKE_CASE__										=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
        SCREAMING_SNAKE_CASE__										=      references[: len(eval_dataloader.dataset	) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase ,     references=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      metric.compute()
  return eval_metric["accuracy"]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  SCREAMING_SNAKE_CASE__										=      config["""lr"""]
  SCREAMING_SNAKE_CASE__										=      int(config["""num_epochs"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""seed"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""batch_size"""]	)
  SCREAMING_SNAKE_CASE__										=      args.model_name_or_path

  set_seed(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_dataloaders(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE__										=      AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,     return_dict=__UpperCamelCase	)

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__										=      (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE__										=      optimizer_cls(params=model.parameters() ,     lr=__UpperCamelCase	)

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__										=      accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      (len(__UpperCamelCase	) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    SCREAMING_SNAKE_CASE__										=      get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase ,     num_warmup_steps=0 ,     num_training_steps=__UpperCamelCase ,     )
  else:
    SCREAMING_SNAKE_CASE__										=      DummyScheduler(__UpperCamelCase ,     total_num_steps=__UpperCamelCase ,     warmup_num_steps=0	)

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.prepare(
      __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # We need to keep track of how many total steps we have iterated over
  SCREAMING_SNAKE_CASE__										=      0
  # We also need to keep track of the stating epoch so files are named properly
  SCREAMING_SNAKE_CASE__										=      0
  SCREAMING_SNAKE_CASE__										=      evaluate.load("""glue""" ,     """mrpc"""	)
  SCREAMING_SNAKE_CASE__										=      num_epochs

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__										=      args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint	)
    SCREAMING_SNAKE_CASE__										=      args.resume_from_checkpoint.split("""epoch_"""	)[1]
    SCREAMING_SNAKE_CASE__										=      """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__										=      int(__UpperCamelCase	) + 1
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint performance:""" ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint's scheduler's lr:""" ,     lr_scheduler.get_lr()[0]	)
    accelerator.print("""resumed optimizers's lr:""" ,     optimizer.param_groups[0]["""lr"""]	)
    with open(os.path.join(args.output_dir ,     f"""state_{starting_epoch-1}.json"""	) ,     """r"""	) as f:
      SCREAMING_SNAKE_CASE__										=      json.load(__UpperCamelCase	)
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  SCREAMING_SNAKE_CASE__										=      {}
  for epoch in range(__UpperCamelCase ,     __UpperCamelCase	):
    model.train()
    for step, batch in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      outputs.loss
      SCREAMING_SNAKE_CASE__										=      loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__										=      f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__										=      os.path.join(args.output_dir ,     __UpperCamelCase	)
    accelerator.save_state(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      accuracy
    SCREAMING_SNAKE_CASE__										=      lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__										=      optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__										=      epoch
    SCREAMING_SNAKE_CASE__										=      overall_step
    accelerator.print(f"""epoch {epoch}:""" ,     __UpperCamelCase	)

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir ,     f"""state_{epoch}.json"""	) ,     """w"""	) as f:
        json.dump(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
  parser.add_argument(
      """--model_name_or_path""" ,     type=__UpperCamelCase ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=__UpperCamelCase ,     )
  parser.add_argument(
      """--output_dir""" ,     type=__UpperCamelCase ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
  parser.add_argument(
      """--resume_from_checkpoint""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If the training should continue from a checkpoint folder.""" ,     )
  parser.add_argument(
      """--partial_train_epoch""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If passed, the training will stop after this number of epochs.""" ,     )
  parser.add_argument(
      """--num_epochs""" ,     type=__UpperCamelCase ,     default=2 ,     help="""Number of train epochs.""" ,     )
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  SCREAMING_SNAKE_CASE__										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(__UpperCamelCase ,     __UpperCamelCase	)


if __name__ == "__main__":
   main()
