


'''simple docstring'''


import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_UpperCamelCase 							=  """\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

_UpperCamelCase 							=  """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

_UpperCamelCase 							=  """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""







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

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return datasets.MetricInfo(
              description=_DESCRIPTION			,     citation=_CITATION			,     homepage="https://github.com/krishnap25/mauve"			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"			,     id="sequence"		),
                      "references": datasets.Value("string"			,     id="sequence"		),
                  }		)			,     codebase_urls=["https://github.com/krishnap25/mauve"]			,     reference_urls=[
                  "https://arxiv.org/abs/2102.01454",
                  "https://github.com/krishnap25/mauve",
              ]			,     )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase="auto"			,     lowerCamelCase=-1			,     lowerCamelCase=0.9			,     lowerCamelCase=5			,     lowerCamelCase=500			,     lowerCamelCase="gpt2-large"			,     lowerCamelCase=-1			,     lowerCamelCase=1024			,     lowerCamelCase=25			,     lowerCamelCase=5			,     lowerCamelCase=True			,     lowerCamelCase=25			,     ):


          '''simple docstring'''





          __A	:       Dict      =							compute_mauve(
              p_text=lowerCamelCase			,     q_text=lowerCamelCase			,     p_features=lowerCamelCase			,     q_features=lowerCamelCase			,     p_tokens=lowerCamelCase			,     q_tokens=lowerCamelCase			,     num_buckets=lowerCamelCase			,     pca_max_data=lowerCamelCase			,     kmeans_explained_var=lowerCamelCase			,     kmeans_num_redo=lowerCamelCase			,     kmeans_max_iter=lowerCamelCase			,     featurize_model_name=lowerCamelCase			,     device_id=lowerCamelCase			,     max_text_length=lowerCamelCase			,     divergence_curve_discretization_size=lowerCamelCase			,     mauve_scaling_factor=lowerCamelCase			,     verbose=lowerCamelCase			,     seed=lowerCamelCase			,     )
          return out





'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase 							=  16
_UpperCamelCase 							=  32


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = 16							):




     '''simple docstring'''





     __A	:       Tuple      =							AutoTokenizer.from_pretrained("bert-base-cased"							)
     __A	:       List[str]      =							load_dataset("glue"       ,					"mrpc"							)

     def tokenize_function(SCREAMING_SNAKE_CASE							):
          # max_length=None => use the model max length (it's actually the default)
          __A	:       Any      =							tokenizer(examples["sentence1"]       ,					examples["sentence2"]       ,					truncation=SCREAMING_SNAKE_CASE       ,					max_length=SCREAMING_SNAKE_CASE							)
          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	:       Optional[Any]      =							datasets.map(
              SCREAMING_SNAKE_CASE       ,					batched=SCREAMING_SNAKE_CASE       ,					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(SCREAMING_SNAKE_CASE							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A	:       str      =							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":
               __A	:       List[str]      =							16
          elif accelerator.mixed_precision != "no":
               __A	:       Tuple      =							8
          else:
               __A	:       Optional[Any]      =							None

          return tokenizer.pad(
              SCREAMING_SNAKE_CASE       ,					padding="longest"       ,					max_length=SCREAMING_SNAKE_CASE       ,					pad_to_multiple_of=SCREAMING_SNAKE_CASE       ,					return_tensors="pt"       ,					)

     # Instantiate dataloaders.
     __A	:       Optional[int]      =							DataLoader(
         tokenized_datasets["train"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							DataLoader(
         tokenized_datasets["validation"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)

     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

      _UpperCamelCase 							=  mocked_dataloaders  # noqa: F811


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,					SCREAMING_SNAKE_CASE							) == "1":
          __A	:       int      =							2
     # New Code #
     __A	:       List[str]      =							int(args.gradient_accumulation_steps							)
     __A	:       int      =							int(args.local_sgd_steps							)
     # Initialize accelerator
     __A	:       int      =							Accelerator(
         cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A	:       Tuple      =							config["lr"]
     __A	:       Union[str, Any]      =							int(config["num_epochs"]							)
     __A	:       Optional[int]      =							int(config["seed"]							)
     __A	:       Optional[Any]      =							int(config["batch_size"]							)

     __A	:       Optional[int]      =							evaluate.load("glue"       ,					"mrpc"							)

     set_seed(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Tuple      =							get_dataloaders(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A	:       List[str]      =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,					return_dict=SCREAMING_SNAKE_CASE							)

     # 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	:       Union[str, Any]      =							model.to(accelerator.device							)

     # Instantiate optimizer
     __A	:       str      =							AdamW(params=model.parameters()       ,					lr=SCREAMING_SNAKE_CASE							)

     # Instantiate scheduler
     __A	:       Optional[int]      =							get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE       ,					num_warmup_steps=100       ,					num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs)       ,					)

     # 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	:       List[str]      =							accelerator.prepare(
         SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE							):
          model.train()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE       ,					model=SCREAMING_SNAKE_CASE       ,					local_sgd_steps=SCREAMING_SNAKE_CASE       ,					enabled=local_sgd_steps is not None							) as local_sgd:
               for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device							)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                         __A	:       List[Any]      =							model(**SCREAMING_SNAKE_CASE							)
                         __A	:       List[str]      =							output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
               # 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(**SCREAMING_SNAKE_CASE							)
               __A	:       Union[str, Any]      =							outputs.logits.argmax(dim=-1							)
               __A  ,__A	:       Tuple      =							accelerator.gather_for_metrics((predictions, batch["labels"])							)
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE       ,					references=SCREAMING_SNAKE_CASE       ,					)

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


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"       ,					type=SCREAMING_SNAKE_CASE       ,					default=SCREAMING_SNAKE_CASE       ,					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."       ,					)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="The number of minibatches to be ran before gradients are accumulated."       ,					)
     parser.add_argument(
         "--local_sgd_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8       ,					help="Number of local SGD steps or None to disable local SGD"							)
     parser.add_argument("--cpu"       ,					action="store_true"       ,					help="If passed, will train on the CPU."							)
     __A	:       List[str]      =							parser.parse_args()
     __A	:       List[str]      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      main()

