


import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


    """simple docstring"""





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

    def tokenize_function(lowercase						:						str				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[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
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case					:   Dict         =					datasets.map(
            _lowerCamelCase		,							batched=_lowerCamelCase		,							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					:   List[Any]         =					tokenized_datasets.rename_column("label"		,							"labels"				)

    def collate_fn(lowercase						:						Optional[Any]				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case					:   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":
            snake_case					:   Tuple         =					16
        elif accelerator.mixed_precision != "no":
            snake_case					:   str         =					8
        else:
            snake_case					:   Optional[Any]         =					None

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

    # Instantiate dataloaders.
    snake_case					:   List[str]         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=_lowerCamelCase		,							collate_fn=_lowerCamelCase		,							batch_size=_lowerCamelCase				)
    snake_case					:   List[Any]         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=_lowerCamelCase		,							collate_fn=_lowerCamelCase		,							batch_size=_lowerCamelCase				)

    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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						str		,							lowercase						:						Any				)			->		Any:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							_lowerCamelCase				) == "1":
        snake_case					:   Tuple         =					2
    # Initialize accelerator
    snake_case					:   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					:   Dict         =					config["lr"]
    snake_case					:   str         =					int(config["num_epochs"]				)
    snake_case					:   int         =					int(config["seed"]				)
    snake_case					:   List[Any]         =					int(config["batch_size"]				)

    snake_case					:   Any         =					evaluate.load("glue"		,							"mrpc"				)

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=_lowerCamelCase				)
    def inner_training_loop(lowercase						:						Any				):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(_lowerCamelCase				)

        # 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=_lowerCamelCase				)

        # 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					:   Optional[int]         =					model.to(accelerator.device				)

        # Instantiate optimizer
        snake_case					:   Optional[Any]         =					AdamW(params=model.parameters()		,							lr=_lowerCamelCase				)
        snake_case					:   Union[str, Any]         =					get_dataloaders(_lowerCamelCase		,							_lowerCamelCase				)

        # Instantiate scheduler
        snake_case					:   Union[str, Any]         =					get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase		,							num_warmup_steps=100		,							num_training_steps=(len(_lowerCamelCase				) * 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.
        snake_case					:   Optional[int]         =					accelerator.prepare(
            _lowerCamelCase		,							_lowerCamelCase		,							_lowerCamelCase		,							_lowerCamelCase		,							_lowerCamelCase				)

        # Now we train the model
        for epoch in range(_lowerCamelCase				):
            model.train()
            for step, batch in enumerate(_lowerCamelCase				):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device				)
                snake_case					:   Union[str, Any]         =					model(**_lowerCamelCase				)
                snake_case					:   Dict         =					outputs.loss
                accelerator.backward(_lowerCamelCase				)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            model.eval()
            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():
                    snake_case					:   Optional[Any]         =					model(**_lowerCamelCase				)
                snake_case					:   Any         =					outputs.logits.argmax(dim=-1				)
                snake_case					:   List[Any]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
                metric.add_batch(
                    predictions=_lowerCamelCase		,							references=_lowerCamelCase		,							)

            snake_case					:   List[str]         =					metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'		,							_lowerCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()







def 						__lowerCAmelCase		(		)			->		Optional[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=_lowerCamelCase		,							default=_lowerCamelCase		,							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."				)
    snake_case					:   str         =					parser.parse_args()
    snake_case					:   Optional[int]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase		,							_lowerCamelCase				)


if __name__ == "__main__":
  main()









"""simple docstring"""



from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
  import importlib_metadata
else:
  import importlib.metadata as importlib_metadata


__snake_case         =					""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):







  class _lowerCAmelCase       (   tr.AbstractTransform			):







     def __init__(			self		,			UpperCamelCase__ = " "		)			->	Any:

         '''simple docstring'''




         snake_case					:   Optional[Any]         =					sentence_delimiter







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

         '''simple docstring'''




         return list(UpperCamelCase__		)







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

         '''simple docstring'''




         snake_case					:   Dict         =					[]
         for sent_idx, sentence in enumerate(UpperCamelCase__		):
             chars.extend(self.process_string(UpperCamelCase__		)		)
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase__		) - 1:
                 chars.append(self.sentence_delimiter		)
         return chars



  __snake_case         =					tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
else:
  __snake_case         =					tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )


__snake_case         =					"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

__snake_case         =					"""\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

__snake_case         =					"""
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""







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







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION		,			citation=_CITATION		,			inputs_description=_KWARGS_DESCRIPTION		,			features=datasets.Features(
               {
                   "predictions": datasets.Value("string"		,			id="sequence"		),
                   "references": datasets.Value("string"		,			id="sequence"		),
               }		)		,			codebase_urls=["https://github.com/jitsi/jiwer/"]		,			reference_urls=[
               "https://en.wikipedia.org/wiki/Word_error_rate",
               "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
           ]		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=False		)			->	Optional[int]:

       '''simple docstring'''




       if concatenate_texts:
           return jiwer.compute_measures(
               UpperCamelCase__		,			UpperCamelCase__		,			truth_transform=UpperCamelCase__		,			hypothesis_transform=UpperCamelCase__		,			)["wer"]

       snake_case					:   Optional[int]         =					0
       snake_case					:   int         =					0
       for prediction, reference in zip(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Dict         =					jiwer.compute_measures(
               UpperCamelCase__		,			UpperCamelCase__		,			truth_transform=UpperCamelCase__		,			hypothesis_transform=UpperCamelCase__		,			)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total





