




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_ : Union[str, Any]    		=       True
except (ImportError, AttributeError):
    snake_case_ : str    		=       object


    def    A						(*__A   :    Tuple   ,				**__A   :    List[str]       )   ->      List[str]:







         """simple docstring"""


         pass


    snake_case_ : List[str]    		=       False


snake_case_ : Tuple    		=       logging.get_logger("transformers-cli/serving")


def    A						(__A   :    Namespace       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							pipeline(
         task=args.task   ,				model=args.model if args.model else None   ,				config=args.config   ,				tokenizer=args.tokenizer   ,				device=args.device   ,				)
     return ServeCommand(__A   ,				args.host   ,				args.port   ,				args.workers       )







class __snake_case			(			a		):
   UpperCAmelCase__  :    dict







class __snake_case			(			a		):
   UpperCAmelCase__  :    List[str]
   UpperCAmelCase__  :    Optional[List[int]]







class __snake_case			(			a		):
   UpperCAmelCase__  :    str







class __snake_case			(			a		):
   UpperCAmelCase__  :    Any







class __snake_case			(			a		):







   @staticmethod
   def lowerCamelCase						(      _snake_case		:						ArgumentParser):

        """simple docstring"""


        UpperCAmelCase_          =							parser.add_parser(
            '''serve'''					,						help='''CLI tool to run inference requests through REST and GraphQL endpoints.''')
        serve_parser.add_argument(
            '''--task'''					,						type=_snake_case					,						choices=get_supported_tasks()					,						help='''The task to run the pipeline on'''					,						)
        serve_parser.add_argument('''--host'''					,						type=_snake_case					,						default='''localhost'''					,						help='''Interface the server will listen on.''')
        serve_parser.add_argument('''--port'''					,						type=_snake_case					,						default=8888					,						help='''Port the serving will listen to.''')
        serve_parser.add_argument('''--workers'''					,						type=_snake_case					,						default=1					,						help='''Number of http workers''')
        serve_parser.add_argument('''--model'''					,						type=_snake_case					,						help='''Model\'s name or path to stored model.''')
        serve_parser.add_argument('''--config'''					,						type=_snake_case					,						help='''Model\'s config name or path to stored model.''')
        serve_parser.add_argument('''--tokenizer'''					,						type=_snake_case					,						help='''Tokenizer name to use.''')
        serve_parser.add_argument(
            '''--device'''					,						type=_snake_case					,						default=-1					,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''					,						)
        serve_parser.set_defaults(func=_snake_case)







   def __init__(      self		:						Tuple					,						_snake_case		:						Pipeline					,						_snake_case		:						str					,						_snake_case		:						int					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							pipeline

        UpperCAmelCase_          =							host
        UpperCAmelCase_          =							port
        UpperCAmelCase_          =							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_          =							FastAPI(
                 routes=[
                     APIRoute(
                         '''/'''					,						self.model_info					,						response_model=_snake_case					,						response_class=_snake_case					,						methods=['''GET''']					,						),
                     APIRoute(
                         '''/tokenize'''					,						self.tokenize					,						response_model=_snake_case					,						response_class=_snake_case					,						methods=['''POST''']					,						),
                     APIRoute(
                         '''/detokenize'''					,						self.detokenize					,						response_model=_snake_case					,						response_class=_snake_case					,						methods=['''POST''']					,						),
                     APIRoute(
                         '''/forward'''					,						self.forward					,						response_model=_snake_case					,						response_class=_snake_case					,						methods=['''POST''']					,						),
                 ]					,						timeout=600					,						)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						str = Body(_snake_case					,						embed=_snake_case)					,						_snake_case		:						bool = Body(_snake_case					,						embed=_snake_case)):

        """simple docstring"""


        try:
             UpperCAmelCase_          =							self._pipeline.tokenizer.tokenize(_snake_case)

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

        except Exception as e:
             raise HTTPException(status_code=500					,						detail={'''model''': '''''', '''error''': str(_snake_case)})







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						List[int] = Body(_snake_case					,						embed=_snake_case)					,						_snake_case		:						bool = Body(_snake_case					,						embed=_snake_case)					,						_snake_case		:						bool = Body(_snake_case					,						embed=_snake_case)					,						):

        """simple docstring"""


        try:
             UpperCAmelCase_          =							self._pipeline.tokenizer.decode(_snake_case					,						_snake_case					,						_snake_case)
             return ServeDeTokenizeResult(model=''''''					,						text=_snake_case)
        except Exception as e:
             raise HTTPException(status_code=500					,						detail={'''model''': '''''', '''error''': str(_snake_case)})







   async def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Optional[int]=Body(_snake_case					,						embed=_snake_case)):

        """simple docstring"""


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

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








'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowerCAmelCase         =    1_6
__lowerCAmelCase         =    3_2





def 					UpperCAmelCase_		(__a      :    Accelerator ,	__a      :    DatasetDict ,	__a      :    List[int] ,	__a      :    List[int] ,	__a      :    int = 1_6 ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   AutoTokenizer.from_pretrained('bert-base-cased' )
   _a				:	str			=   DatasetDict(
       {
           'train': dataset['train'].select(__a ),
           'validation': dataset['train'].select(__a ),
           'test': dataset['validation'],
       } )

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _a				:	List[str]			=   datasets.map(
          __a ,	batched=__a ,	remove_columns=['idx', 'sentence1', 'sentence2'] ,	)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   _a				:	List[Any]			=   tokenized_datasets.rename_column('label' ,	'labels' )

   def collate_fn(__a      :    int ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _a				:	Dict			=   1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _a				:	Tuple			=   1_6
      elif accelerator.mixed_precision != "no":
         _a				:	List[Any]			=   8
      else:
         _a				:	List[Any]			=   None

      return tokenizer.pad(
          __a ,	padding='longest' ,	max_length=__a ,	pad_to_multiple_of=__a ,	return_tensors='pt' ,	)

   # Instantiate dataloaders.
   _a				:	Any			=   DataLoader(
       tokenized_datasets['train'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )
   _a				:	Optional[int]			=   DataLoader(
       tokenized_datasets['validation'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   _a				:	Optional[Any]			=   DataLoader(
       tokenized_datasets['test'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   return train_dataloader, eval_dataloader, test_dataloader





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






   """simple docstring"""

   _a				:	Dict			=   []
   # Download the dataset
   _a				:	Tuple			=   load_dataset('glue' ,	'mrpc' )
   # Create our splits
   _a				:	Union[str, Any]			=   StratifiedKFold(n_splits=int(args.num_folds ) )
   # Initialize accelerator
   _a				:	Any			=   Accelerator(cpu=args.cpu ,	mixed_precision=args.mixed_precision )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _a				:	Optional[Any]			=   config['lr']
   _a				:	Optional[int]			=   int(config['num_epochs'] )
   _a				:	Dict			=   int(config['seed'] )
   _a				:	Dict			=   int(config['batch_size'] )

   _a				:	Optional[int]			=   evaluate.load('glue' ,	'mrpc' )

   # If the batch size is too big we use gradient accumulation
   _a				:	List[Any]			=   1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _a				:	Any			=   batch_size // MAX_GPU_BATCH_SIZE
      _a				:	List[str]			=   MAX_GPU_BATCH_SIZE

   set_seed(__a )

   # New Code #
   # Create our folds:
   _a				:	int			=   kfold.split(np.zeros(datasets['train'].num_rows ) ,	datasets['train']['label'] )
   _a				:	Any			=   []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(__a ):
      _a,  _a,  _a				:	Optional[Any]			=   get_fold_dataloaders(
          __a ,	__a ,	__a ,	__a ,	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _a				:	Dict			=   AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,	return_dict=__a )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      _a				:	List[Any]			=   model.to(accelerator.device )

      # Instantiate optimizer
      _a				:	List[str]			=   AdamW(params=model.parameters() ,	lr=__a )

      # Instantiate scheduler
      _a				:	List[Any]			=   get_linear_schedule_with_warmup(
          optimizer=__a ,	num_warmup_steps=1_0_0 ,	num_training_steps=(len(__a ) * num_epochs) // gradient_accumulation_steps ,	)

      # 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.
      _a,  _a,  _a,  _a,  _a				:	Union[str, Any]			=   accelerator.prepare(
          __a ,	__a ,	__a ,	__a ,	__a )

      # Now we train the model
      for epoch in range(__a ):
         model.train()
         for step, batch in enumerate(__a ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            _a				:	Dict			=   model(**__a )
            _a				:	int			=   outputs.loss
            _a				:	Any			=   loss / gradient_accumulation_steps
            accelerator.backward(__a )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(__a ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            with torch.no_grad():
               _a				:	Union[str, Any]			=   model(**__a )
            _a				:	Tuple			=   outputs.logits.argmax(dim=-1 )
            _a,  _a				:	Any			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=__a ,	references=__a ,	)

         _a				:	List[Any]			=   metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f"""epoch {epoch}:""" ,	__a )

      # New Code #
      # We also run predictions on the test set at the very end
      _a				:	Any			=   []
      for step, batch in enumerate(__a ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device )
         with torch.no_grad():
            _a				:	Tuple			=   model(**__a )
         _a				:	Dict			=   outputs.logits
         _a,  _a				:	Optional[int]			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
         fold_predictions.append(predictions.cpu() )
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(__a ,	dim=0 ) )
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   _a				:	Dict			=   torch.cat(__a ,	dim=0 )
   _a				:	Any			=   torch.stack(__a ,	dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
   _a				:	str			=   metric.compute(predictions=__a ,	references=__a )
   accelerator.print('Average test metrics from all folds:' ,	__a )





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Any			=   argparse.ArgumentParser(description='Simple example of training script.' )
   parser.add_argument(
       '--mixed_precision' ,	type=__a ,	default=__a ,	choices=['no', 'fp16', 'bf16', 'fp8'] ,	help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.' ,	)
   parser.add_argument('--cpu' ,	action='store_true' ,	help='If passed, will train on the CPU.' )
   # New Code #
   parser.add_argument('--num_folds' ,	type=__a ,	default=3 ,	help='The number of splits to perform across the dataset' )
   _a				:	Any			=   parser.parse_args()
   _a				:	int			=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
   training_function(__a ,	__a )


if __name__ == "__main__":
  main()



