
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


a_											=						"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

a_											=						"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

a_											=						"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"






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





 def        snake_case__		(    self):
     if version.parse(scb.__version__) < version.parse("1.4.12"):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`.")
     return datasets.MetricInfo(
         description=_DESCRIPTION ,       citation=_CITATION ,       homepage="http://www.cs.umd.edu/~snover/tercom/" ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
             {
                 "predictions": datasets.Value("string" ,       id="sequence"),
                 "references": datasets.Sequence(datasets.Value("string" ,       id="sequence") ,       id="references"),
             }) ,       codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"] ,       reference_urls=[
             "https://github.com/jhclark/tercom",
         ] ,       )





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = False ,       lowercase_ = False ,       lowercase_ = False ,       lowercase_ = False ,       ):
     snake_case_ :    int     			=						len(references[0])
     if any(len(lowercase_) != references_per_prediction for refs in references):
         raise ValueError("Sacrebleu requires the same number of references for each prediction")
     snake_case_ :    List[str]     			=						[[refs[i] for refs in references] for i in range(lowercase_)]

     snake_case_ :    int     			=						TER(
         normalized=lowercase_ ,       no_punct=lowercase_ ,       asian_support=lowercase_ ,       case_sensitive=lowercase_ ,       )
     snake_case_ :    Union[str, Any]     			=						sb_ter.corpus_score(lowercase_ ,       lowercase_)

     return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






'''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
#
########################################################################


a_											=						16
a_											=						32

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    int     			=						DatasetDict(
        {
            "train": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "validation": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "test": dataset["validation"],
        }       )

    def tokenize_function(__SCREAMING_SNAKE_CASE       ):
        # max_length=None => use the model max length (it's actually the default)
        snake_case_ :    str     			=						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():
        snake_case_ :    List[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
    snake_case_ :    Dict     			=						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.
        snake_case_ :    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":
            snake_case_ :    Optional[int]     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    Tuple     			=						8
        else:
            snake_case_ :    Union[str, 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.
    snake_case_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["test"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    return train_dataloader, eval_dataloader, test_dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    snake_case_ :    List[Any]     			=						evaluate.load("glue",		"mrpc"       )

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

    set_seed(__SCREAMING_SNAKE_CASE       )

    # New Code #
    # Create our folds:
    snake_case_ :    int     			=						kfold.split(np.zeros(datasets["train"].num_rows       ),		datasets["train"]["label"]       )
    snake_case_ :    Optional[int]     			=						[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						get_fold_dataloaders(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		)
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        snake_case_ :    Optional[int]     			=						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).
        snake_case_ :    Union[str, Any]     			=						model.to(accelerator.device       )

        # Instantiate optimizer
        snake_case_ :    Any     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        snake_case_ :    List[str]     			=						get_linear_schedule_with_warmup(
            optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		num_training_steps=(len(__SCREAMING_SNAKE_CASE       ) * 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.
        snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_ :    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()
            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       )
                snake_case_ :    List[Any]     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    Union[str, Any]     			=						outputs.loss
                snake_case_ :    Union[str, Any]     			=						loss / gradient_accumulation_steps
                accelerator.backward(__SCREAMING_SNAKE_CASE       )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            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():
                    snake_case_ :    Dict     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    str     			=						outputs.logits.argmax(dim=-1       )
                snake_case_			, snake_case_ :    Dict     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
                metric.add_batch(
                    predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE,		)

            snake_case_ :    str     			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:',		__SCREAMING_SNAKE_CASE       )

        # New Code #
        # We also run predictions on the test set at the very end
        snake_case_ :    Any     			=						[]
        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():
                snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Tuple     			=						outputs.logits
            snake_case_			, snake_case_ :    List[Any]     			=						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(__SCREAMING_SNAKE_CASE,		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:
    snake_case_ :    List[Any]     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    snake_case_ :    Any     			=						torch.stack(__SCREAMING_SNAKE_CASE,		dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    snake_case_ :    Tuple     			=						metric.compute(predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE       )
    accelerator.print("Average test metrics from all folds:",		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						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.",		)
    parser.add_argument("--cpu",		action="store_true",		help="If passed, will train on the CPU."       )
    # New Code #
    parser.add_argument("--num_folds",		type=__SCREAMING_SNAKE_CASE,		default=3,		help="The number of splits to perform across the dataset"       )
    snake_case_ :    List[Any]     			=						parser.parse_args()
    snake_case_ :    Optional[Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()




