
'''simple docstring'''



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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

UpperCamelCase__					:						Tuple											=     32
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = 16  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = AutoTokenizer.from_pretrained("""bert-base-cased"""  )
   _SCREAMING_SNAKE_CASE          = load_dataset("""glue"""			,					"""mrpc"""  )

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

   def collate_fn(SCREAMING_SNAKE_CASE_  ):
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _SCREAMING_SNAKE_CASE          = 16
      elif accelerator.mixed_precision != "no":
         _SCREAMING_SNAKE_CASE          = 8
      else:
         _SCREAMING_SNAKE_CASE          = None

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

   # Instantiate dataloaders.
   _SCREAMING_SNAKE_CASE          = DataLoader(
       tokenized_datasets["""train"""]			,					shuffle=SCREAMING_SNAKE_CASE_			,					collate_fn=SCREAMING_SNAKE_CASE_			,					batch_size=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = 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__					:						List[str]											=     mocked_dataloaders  # noqa: F811
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,					SCREAMING_SNAKE_CASE_  ) == "1":
      _SCREAMING_SNAKE_CASE          = 2
   # Initialize accelerator
   _SCREAMING_SNAKE_CASE          = Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision  )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _SCREAMING_SNAKE_CASE          = config["""lr"""]
   _SCREAMING_SNAKE_CASE          = int(config["""num_epochs"""]  )
   _SCREAMING_SNAKE_CASE          = int(config["""seed"""]  )
   _SCREAMING_SNAKE_CASE          = int(config["""batch_size"""]  )

   _SCREAMING_SNAKE_CASE          = evaluate.load("""glue"""			,					"""mrpc"""  )

   # New Code #
   # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
   # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
   # half each time. From this, we can calculate the number of gradient accumulation steps needed
   # and modify the Accelerator object as a result
   @find_executable_batch_size(starting_batch_size=int(SCREAMING_SNAKE_CASE_  )  )
   def inner_training_loop(SCREAMING_SNAKE_CASE_  ):
      # Since we need to modify the outside accelerator object, we need to bring it
      # to the local scope
      nonlocal accelerator

      # We can calculate the number of gradient accumulation steps based on the current
      # batch size vs the starting batch size
      _SCREAMING_SNAKE_CASE          = observed_batch_size // batch_size

      # And then set it in the Accelerator directly:
      _SCREAMING_SNAKE_CASE          = num_gradient_accumulation_steps

      # Next we need to free all of the stored model references in the Accelerator each time
      accelerator.free_memory()

      # And set the seed so our results are reproducable each reset
      set_seed(SCREAMING_SNAKE_CASE_  )

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

      # Instantiate optimizer
      _SCREAMING_SNAKE_CASE          = AdamW(params=model.parameters()			,					lr=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = get_dataloaders(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

      # Instantiate scheduler
      _SCREAMING_SNAKE_CASE          = get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE_			,					num_warmup_steps=1_00			,					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.
      _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = 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_  ):
            # And perform gradient accumulation
            with accelerator.accumulate(SCREAMING_SNAKE_CASE_  ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device  )
               _SCREAMING_SNAKE_CASE          = model(**SCREAMING_SNAKE_CASE_  )
               _SCREAMING_SNAKE_CASE          = outputs.loss
               accelerator.backward(SCREAMING_SNAKE_CASE_  )
               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():
               _SCREAMING_SNAKE_CASE          = model(**SCREAMING_SNAKE_CASE_  )
            _SCREAMING_SNAKE_CASE          = outputs.logits.argmax(dim=-1  )
            _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE_			,					references=SCREAMING_SNAKE_CASE_			,					)

         _SCREAMING_SNAKE_CASE          = metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"epoch {epoch}:"			,					SCREAMING_SNAKE_CASE_  )

    # 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_		( )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = 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."""  )
   _SCREAMING_SNAKE_CASE          = parser.parse_args()
   # New Code #
   # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
   _SCREAMING_SNAKE_CASE          = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 2_56}
   training_function(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     main()







from typing import List

from packaging import version
from sklearn.metrics import fa_score

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


# TODO(Patrick/Anton)
lowerCamelCase_				:		List[str]       						=       """\
"""

lowerCamelCase_				:		Any       						=       """\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

lowerCamelCase_				:		Tuple       						=       """
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

lowerCamelCase_				:		Dict       						=       ["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
lowerCamelCase_				:		Optional[Any]       						=       """"""

try:
	from jiwer import transforms as tr

	lowerCamelCase_				:		str       						=       True
except ImportError:
	lowerCamelCase_				:		List[Any]       						=       False

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


	class 						a__							(   tr.AbstractTransform		):



				def __init__(						self      ,						UpperCAmelCase = " "    ) ->    List[str]:
									__a							=   sentence_delimiter



				def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
									return list(UpperCAmelCase    )







				def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
									__a							=   []
									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






	lowerCamelCase_				:		Any       						=       tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
elif _jiwer_available:
	lowerCamelCase_				:		Optional[Any]       						=       tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)
else:
	lowerCamelCase_				:		Tuple       						=       None
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return float((preds == labels).mean()						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return {
					    "f1": float(fa_score(y_true=__lowerCamelCase      ,				y_pred=__lowerCamelCase      ,				average='macro'						)						),
					    "accuracy": simple_accuracy(__lowerCamelCase      ,				__lowerCamelCase						),
					}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="exp"      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				__lowerCamelCase=False      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				):
					# xtreme-s can only have one label
					__a							=   [[label] for label in labels]
					__a							=   list(__lowerCamelCase						)
					try:
										import sacrebleu as scb
					except ImportError:
										raise ValueError(
										    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
										    'You can install it via `pip install sacrebleu`.'						)

					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"`.'						)

					__a							=   len(labels[0]						)
					if any(len(__lowerCamelCase						) != references_per_prediction for refs in labels						):
										raise ValueError('Sacrebleu requires the same number of references for each prediction'						)
					__a							=   [[refs[i] for refs in labels] for i in range(__lowerCamelCase						)]
					__a							=   scb.corpus_bleu(
					    __lowerCamelCase      ,				__lowerCamelCase      ,				smooth_method=__lowerCamelCase      ,				smooth_value=__lowerCamelCase      ,				force=__lowerCamelCase      ,				lowercase=__lowerCamelCase      ,				use_effective_order=__lowerCamelCase      ,				**({'tokenize': tokenize} if tokenize else {})      ,				)
					return {"bleu": output.score}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					try:
										from jiwer import compute_measures
					except ImportError:
										raise ValueError(
										    f'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
										    'You can install it via `pip install jiwer`.'						)

					if concatenate_texts:
										__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)['wer']

										__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase      ,				truth_transform=__lowerCamelCase      ,				hypothesis_transform=__lowerCamelCase						)['wer']
										return {"wer": wer, "cer": cer}
					else:

										def compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="wer"						):
															__a							=   0
															__a							=   0
															for prediction, reference in zip(__lowerCamelCase      ,				__lowerCamelCase						):
																				if score_type == "wer":
																									__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)
																				elif score_type == "cer":
																									__a							=   compute_measures(
																									    __lowerCamelCase      ,				__lowerCamelCase      ,				truth_transform=__lowerCamelCase      ,				hypothesis_transform=__lowerCamelCase						)
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
															return incorrect / total

										return {"wer": compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				'wer'						), "cer": compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				'cer'						)}



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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								if self.config_name not in _CONFIG_NAMES:
													raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )

								__a							=   'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

								return datasets.MetricInfo(
								    description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
								        {'predictions': datasets.Value(UpperCAmelCase    ), 'references': datasets.Value(UpperCAmelCase    )}    )      ,						codebase_urls=[]      ,						reference_urls=[]      ,						format='numpy'      ,						)







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None    ) ->    Optional[Any]:
								__a							=   bleu_kwargs if bleu_kwargs is not None else {}
								__a							=   wer_kwargs if wer_kwargs is not None else {}

								if self.config_name == "fleurs-lang_id":
													return {"accuracy": simple_accuracy(UpperCAmelCase      ,						UpperCAmelCase    )}
								elif self.config_name == "minds14":
													return fa_and_simple_accuracy(UpperCAmelCase      ,						UpperCAmelCase    )
								elif self.config_name == "covost2":
													__a							=   bleu_kwargs.pop('smooth_method'      ,						'exp'    )
													__a							=   bleu_kwargs.pop('smooth_value'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('force'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('lowercase'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('tokenize'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('use_effective_order'      ,						UpperCAmelCase    )
													return bleu(
													    preds=UpperCAmelCase      ,						labels=UpperCAmelCase      ,						smooth_method=UpperCAmelCase      ,						smooth_value=UpperCAmelCase      ,						force=UpperCAmelCase      ,						lowercase=UpperCAmelCase      ,						tokenize=UpperCAmelCase      ,						use_effective_order=UpperCAmelCase      ,						)
								elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
													__a							=   wer_kwargs.pop('concatenate_texts'      ,						UpperCAmelCase    )
													return wer_and_cer(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						self.config_name    )
								else:
													raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )




