


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


lowercase							=16
lowercase							=32





def lowerCamelCase__      (						__lowerCamelCase	:							Accelerator	,   __lowerCamelCase	:							int = 1_6	,   __lowerCamelCase	:							str = "bert-base-cased"							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =AutoTokenizer.from_pretrained(__lowerCamelCase							)
   _UpperCAmelCase       :       Any							   =load_dataset('glue'	,   'mrpc'							)

   def tokenize_function(__lowerCamelCase	:							Dict							):
      # max_length=None => use the model max length (it's actually the default)
      _UpperCAmelCase       :       str							   =tokenizer(examples['sentence1']	,   examples['sentence2']	,   truncation=__lowerCamelCase	,   max_length=__lowerCamelCase							)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   _UpperCAmelCase       :       Dict							   =datasets.map(
       __lowerCamelCase	,   batched=__lowerCamelCase	,   remove_columns=['idx', 'sentence1', 'sentence2']	,   load_from_cache_file=__lowerCamelCase							)

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

   def collate_fn(__lowerCamelCase	:							int							):
      # 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(__lowerCamelCase	,   padding='max_length'	,   max_length=1_2_8	,   return_tensors='pt'							)
      return tokenizer.pad(__lowerCamelCase	,   padding='longest'	,   return_tensors='pt'							)

   # Instantiate dataloaders.
   _UpperCAmelCase       :       Any							   =DataLoader(
       tokenized_datasets['train']	,   shuffle=__lowerCamelCase	,   collate_fn=__lowerCamelCase	,   batch_size=__lowerCamelCase							)
   _UpperCAmelCase       :       Tuple							   =DataLoader(
       tokenized_datasets['validation']	,   shuffle=__lowerCamelCase	,   collate_fn=__lowerCamelCase	,   batch_size=__lowerCamelCase							)

   return train_dataloader, eval_dataloader





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




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

   _UpperCAmelCase       :       Optional[Any]							   =metric.compute()
   return eval_metric["accuracy"]





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _UpperCAmelCase       :       Dict							   =config['lr']
   _UpperCAmelCase       :       Tuple							   =int(config['num_epochs']							)
   _UpperCAmelCase       :       List[Any]							   =int(config['seed']							)
   _UpperCAmelCase       :       Any							   =int(config['batch_size']							)
   _UpperCAmelCase       :       str							   =args.model_name_or_path

   set_seed(__lowerCamelCase							)
   _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =get_dataloaders(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _UpperCAmelCase       :       Dict							   =AutoModelForSequenceClassification.from_pretrained(__lowerCamelCase	,   return_dict=__lowerCamelCase							)

   # Instantiate optimizer
   _UpperCAmelCase       :       List[str]							   =(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   _UpperCAmelCase       :       List[str]							   =optimizer_cls(params=model.parameters()	,   lr=__lowerCamelCase							)

   if accelerator.state.deepspeed_plugin is not None:
      _UpperCAmelCase       :       List[Any]							   =accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      _UpperCAmelCase       :       Union[str, Any]							   =1
   _UpperCAmelCase       :       Optional[Any]							   =(len(__lowerCamelCase							) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      _UpperCAmelCase       :       int							   =get_linear_schedule_with_warmup(
          optimizer=__lowerCamelCase	,   num_warmup_steps=0	,   num_training_steps=__lowerCamelCase	,   )
   else:
      _UpperCAmelCase       :       int							   =DummyScheduler(__lowerCamelCase	,   total_num_steps=__lowerCamelCase	,   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.
   _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =accelerator.prepare(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)

   # We need to keep track of how many total steps we have iterated over
   _UpperCAmelCase       :       int							   =0
   # We also need to keep track of the stating epoch so files are named properly
   _UpperCAmelCase       :       Dict							   =0
   _UpperCAmelCase       :       Dict							   =evaluate.load('glue'	,   'mrpc'							)
   _UpperCAmelCase       :       Union[str, Any]							   =num_epochs

   if args.partial_train_epoch is not None:
      _UpperCAmelCase       :       int							   =args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint							)
      _UpperCAmelCase       :       Optional[Any]							   =args.resume_from_checkpoint.split('epoch_'							)[1]
      _UpperCAmelCase       :       Dict							   =''
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      _UpperCAmelCase       :       str							   =int(__lowerCamelCase							) + 1
      _UpperCAmelCase       :       Dict							   =evaluation_loop(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
      accelerator.print('resumed checkpoint performance:'	,   __lowerCamelCase							)
      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:
         _UpperCAmelCase       :       List[str]							   =json.load(__lowerCamelCase							)
         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
   _UpperCAmelCase       :       Optional[int]							   ={}
   for epoch in range(__lowerCamelCase	,   __lowerCamelCase							):
      model.train()
      for step, batch in enumerate(__lowerCamelCase							):
         _UpperCAmelCase       :       List[str]							   =model(**__lowerCamelCase							)
         _UpperCAmelCase       :       Any							   =outputs.loss
         _UpperCAmelCase       :       Dict							   =loss / gradient_accumulation_steps
         accelerator.backward(__lowerCamelCase							)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      _UpperCAmelCase       :       str							   =f"epoch_{epoch}"
      _UpperCAmelCase       :       Optional[Any]							   =os.path.join(args.output_dir	,   __lowerCamelCase							)
      accelerator.save_state(__lowerCamelCase							)
      _UpperCAmelCase       :       List[str]							   =evaluation_loop(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
      _UpperCAmelCase       :       Optional[Any]							   =accuracy
      _UpperCAmelCase       :       int							   =lr_scheduler.get_lr()[0]
      _UpperCAmelCase       :       Optional[Any]							   =optimizer.param_groups[0]['lr']
      _UpperCAmelCase       :       Optional[int]							   =epoch
      _UpperCAmelCase       :       List[Any]							   =overall_step
      accelerator.print(f"epoch {epoch}:"	,   __lowerCamelCase							)

      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(__lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
   parser.add_argument(
       '--model_name_or_path'	,   type=__lowerCamelCase	,   default='bert-base-cased'	,   help='Path to pretrained model or model identifier from huggingface.co/models.'	,   required=__lowerCamelCase	,   )
   parser.add_argument(
       '--output_dir'	,   type=__lowerCamelCase	,   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=__lowerCamelCase	,   default=__lowerCamelCase	,   help='If the training should continue from a checkpoint folder.'	,   )
   parser.add_argument(
       '--partial_train_epoch'	,   type=__lowerCamelCase	,   default=__lowerCamelCase	,   help='If passed, the training will stop after this number of epochs.'	,   )
   parser.add_argument(
       '--num_epochs'	,   type=__lowerCamelCase	,   default=2	,   help='Number of train epochs.'	,   )
   _UpperCAmelCase       :       Optional[Any]							   =parser.parse_args()
   _UpperCAmelCase       :       Optional[int]							   ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

   training_function(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 main()





'''simple docstring'''






from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowercase							=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase    )
class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          super().__init__(**snake_case)

          requires_backends(self  ,							'vision')
          self.check_model_type(
              TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)






       def __call__(      self  ,							snake_case  ,							**snake_case)						->  str:




          '''simple docstring'''
          return super().__call__(snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={}
          if "candidate_labels" in kwargs:
             _UpperCAmelCase       :       Union[str, Any]							   =kwargs['candidate_labels']
          if "hypothesis_template" in kwargs:
             _UpperCAmelCase       :       List[Any]							   =kwargs['hypothesis_template']

          return preprocess_params, {}, {}






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="This is a photo of {}.")						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =load_image(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor(images=[image]  ,							return_tensors=self.framework)
          _UpperCAmelCase       :       Union[str, Any]							   =candidate_labels
          _UpperCAmelCase       :       List[Any]							   =[hypothesis_template.format(snake_case) for x in candidate_labels]
          _UpperCAmelCase       :       str							   =self.tokenizer(snake_case  ,							return_tensors=self.framework  ,							padding=snake_case)
          _UpperCAmelCase       :       Any							   =[text_inputs]
          return inputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =model_inputs.pop('candidate_labels')
          _UpperCAmelCase       :       Tuple							   =model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]  ,							snake_case):
             _UpperCAmelCase       :       Any							   =text_inputs[0]
          else:
             # Batching case.
             _UpperCAmelCase       :       str							   =text_inputs[0][0]

          _UpperCAmelCase       :       Any							   =self.model(**snake_case  ,							**snake_case)

          _UpperCAmelCase       :       List[str]							   ={
              'candidate_labels': candidate_labels,
              'logits': outputs.logits_per_image,
          }
          return model_outputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =model_outputs.pop('candidate_labels')
          _UpperCAmelCase       :       Union[str, Any]							   =model_outputs['logits'][0]
          if self.framework == "pt":
             _UpperCAmelCase       :       Dict							   =logits.softmax(dim=-1).squeeze(-1)
             _UpperCAmelCase       :       Union[str, Any]							   =probs.tolist()
             if not isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =[scores]
          elif self.framework == "tf":
             _UpperCAmelCase       :       Dict							   =stable_softmax(snake_case  ,							axis=-1)
             _UpperCAmelCase       :       str							   =probs.numpy().tolist()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase       :       List[str]							   =[
              {'score': score, 'label': candidate_label}
              for score, candidate_label in sorted(zip(snake_case  ,							snake_case)  ,							key=lambda snake_case: -x[0])
          ]
          return result

