



'''simple docstring'''





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

a__      :				Optional[Any]   	=			16
a__      :				Optional[int]   	=			32

def   _lowerCAmelCase   (						A__						,  A__ = 16	):
      lowercase__     =  AutoTokenizer.from_pretrained('bert-base-cased'	)
      lowercase__     =  load_dataset('glue'						,  'mrpc'	)

      def tokenize_function(A__	):
            # max_length=None => use the model max length (it's actually the default)
            lowercase__     =  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():
            lowercase__     =  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
      lowercase__     =  tokenized_datasets.rename_column('label'						,  'labels'	)

      def collate_fn(A__	):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowercase__     =  128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowercase__     =  16
            elif accelerator.mixed_precision != "no":
                  lowercase__     =  8
            else:
                  lowercase__     =  None

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

      # Instantiate dataloaders.
      lowercase__     =  DataLoader(
          tokenized_datasets['train']						,  shuffle=A__						,  collate_fn=A__						,  batch_size=A__	)
      lowercase__     =  DataLoader(
          tokenized_datasets['validation']						,  shuffle=A__						,  collate_fn=A__						,  batch_size=A__	)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       a__      :				Any   	=			mocked_dataloaders  # noqa: F811

def   _lowerCAmelCase   (						A__						,  A__	):
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'						,  A__	) == "1":
            lowercase__     =  2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            lowercase__     =  Accelerator(
                cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with='all'						,  project_dir=args.project_dir	)
      else:
            lowercase__     =  Accelerator(cpu=args.cpu						,  mixed_precision=args.mixed_precision	)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowercase__     =  config['lr']
      lowercase__     =  int(config['num_epochs']	)
      lowercase__     =  int(config['seed']	)
      lowercase__     =  int(config['batch_size']	)
      set_seed(A__	)

      lowercase__,	lowercase__     =  get_dataloaders(A__						,  A__	)
      lowercase__     =  evaluate.load('glue'						,  'mrpc'	)

      # If the batch size is too big we use gradient accumulation
      lowercase__     =  1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowercase__     =  batch_size // MAX_GPU_BATCH_SIZE
            lowercase__     =  MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase__     =  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).
      lowercase__     =  model.to(accelerator.device	)

      # Instantiate optimizer
      lowercase__     =  AdamW(params=model.parameters()						,  lr=A__	)

      # Instantiate scheduler
      lowercase__     =  get_linear_schedule_with_warmup(
          optimizer=A__						,  num_warmup_steps=100						,  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.
      lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
          A__						,  A__						,  A__						,  A__						,  A__	)

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            lowercase__     =  os.path.split(A__	)[-1].split('.'	)[0]
            accelerator.init_trackers(A__						,  A__	)

      # Now we train the model
      for epoch in range(A__	):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  lowercase__     =  0
            for step, batch in enumerate(A__	):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device	)
                  lowercase__     =  model(**A__	)
                  lowercase__     =  outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowercase__     =  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` (the default).
                  batch.to(accelerator.device	)
                  with torch.no_grad():
                        lowercase__     =  model(**A__	)
                  lowercase__     =  outputs.logits.argmax(dim=-1	)
                  lowercase__,	lowercase__     =  accelerator.gather_for_metrics((predictions, batch['labels'])	)
                  metric.add_batch(
                      predictions=A__						,  references=A__						,  )

            lowercase__     =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''						,  A__	)

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          'accuracy': eval_metric['accuracy'],
                          'f1': eval_metric['f1'],
                          'train_loss': total_loss.item() / len(A__	),
                          'epoch': epoch,
                      }						,  step=A__						,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()

def   _lowerCAmelCase   (						):
      lowercase__     =  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.'	)
      parser.add_argument(
          '--with_tracking'						,  action='store_true'						,  help='Whether to load in all available experiment trackers from the environment and use them for logging.'						,  )
      parser.add_argument(
          '--project_dir'						,  type=A__						,  default='logs'						,  help='Location on where to store experiment tracking logs` and relevent project information'						,  )
      lowercase__     =  parser.parse_args()
      lowercase__     =  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(A__						,  A__	)


if __name__ == "__main__":
       main()





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a__      :				int   	=			"\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a__      :				Optional[Any]   	=			"\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a__      :				Tuple   	=			"\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION	)
class       UpperCAmelCase__(							datasets.Metric	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				MetricInfo:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION			,				citation=_CITATION			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('string'			,				id='token')			,				id='sequence'),
                     'references': datasets.Sequence(
                         datasets.Sequence(datasets.Value('string'			,				id='token')			,				id='sequence')			,				id='references'),
                 })			,				)



   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			List[List[List[str]]]			,				lowerCAmelCase  :			List[List[str]]			,				lowerCAmelCase  :			int = 1			,				lowerCAmelCase  :			int = 4			,				)	->				Dict[str, float]:




         """simple docstring"""

         return {
             "google_bleu": gleu_score.corpus_gleu(
                 list_of_references=lowerCAmelCase			,				hypotheses=lowerCAmelCase			,				min_len=lowerCAmelCase			,				max_len=lowerCAmelCase)
         }
