



"""simple docstring"""






import argparse

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
#
# 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
#
########################################################################


_lowercase			:			List[str]										=						1_6
_lowercase			:			List[Any]										=						3_2






def 					snake_case__       (		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Optional[int] = 16     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =AutoTokenizer.from_pretrained('''bert-base-cased'''     )
			lowerCamelCase__		:		Union[str, Any]		     =load_dataset('''glue'''							,		'''mrpc'''     )

			def tokenize_function(__lowerCamelCase    :   Optional[Any]     ):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase__		:		str		     =tokenizer(examples['''sentence1''']							,		examples['''sentence2''']							,		truncation=lowercase_							,		max_length=lowercase_     )
						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():
						lowerCamelCase__		:		Any		     =datasets.map(
						    lowercase_							,		batched=lowercase_							,		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
			lowerCamelCase__		:		Dict		     =tokenized_datasets.rename_column('''label'''							,		'''labels'''     )

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

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

			# Instantiate dataloaders.
			lowerCamelCase__		:		Union[str, Any]		     =DataLoader(
			    tokenized_datasets['''train''']							,		shuffle=lowercase_							,		collate_fn=lowercase_							,		batch_size=lowercase_							,		drop_last=lowercase_     )
			lowerCamelCase__		:		List[Any]		     =DataLoader(
			    tokenized_datasets['''validation''']							,		shuffle=lowercase_							,		collate_fn=lowercase_							,		batch_size=lowercase_							,		drop_last=(accelerator.mixed_precision == '''fp8''')							,		)

			return train_dataloader, eval_dataloader






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =Accelerator(cpu=args.cpu							,		mixed_precision=args.mixed_precision     )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase__		:		Any		     =config['''lr''']
			lowerCamelCase__		:		Dict		     =int(config['''num_epochs''']     )
			lowerCamelCase__		:		int		     =int(config['''seed''']     )
			lowerCamelCase__		:		List[str]		     =int(config['''batch_size''']     )

			lowerCamelCase__		:		Optional[int]		     =evaluate.load('''glue'''							,		'''mrpc'''     )

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

			set_seed(lowercase_     )
			lowerCamelCase__     ,       lowerCamelCase__		:		str		     =get_dataloaders(lowercase_							,		lowercase_     )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase__		:		str		     =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,		return_dict=lowercase_     )

			# 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).
			lowerCamelCase__		:		int		     =model.to(accelerator.device     )
			# Instantiate optimizer
			lowerCamelCase__		:		Union[str, Any]		     =AdamW(params=model.parameters()							,		lr=lowercase_     )

			# Instantiate scheduler
			lowerCamelCase__		:		Dict		     =get_linear_schedule_with_warmup(
			    optimizer=lowercase_							,		num_warmup_steps=100							,		num_training_steps=(len(lowercase_     ) * 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.

			lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =accelerator.prepare(
			    lowercase_							,		lowercase_							,		lowercase_							,		lowercase_							,		lowercase_     )

			# Now we train the model
			for epoch in range(lowercase_     ):
						model.train()
						for step, batch in enumerate(lowercase_     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									lowerCamelCase__		:		List[Any]		     =model(**lowercase_     )
									lowerCamelCase__		:		Optional[Any]		     =outputs.loss
									lowerCamelCase__		:		List[str]		     =loss / gradient_accumulation_steps
									accelerator.backward(lowercase_     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(lowercase_     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									with torch.no_grad():
												lowerCamelCase__		:		int		     =model(**lowercase_     )
									lowerCamelCase__		:		Optional[int]		     =outputs.logits.argmax(dim=-1     )
									lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
									metric.add_batch(
									    predictions=lowercase_							,		references=lowercase_							,		)

						lowerCamelCase__		:		Optional[Any]		     =metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''							,		lowercase_     )






def 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =argparse.ArgumentParser(description='''Simple example of training script.'''     )
			parser.add_argument(
			    '''--mixed_precision'''							,		type=lowercase_							,		default=lowercase_							,		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.'''     )
			lowerCamelCase__		:		int		     =parser.parse_args()
			lowerCamelCase__		:		int		     ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(lowercase_							,		lowercase_     )


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


_lowercase			:			List[str]										=						""


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

		class 	__SCREAMING_SNAKE_CASE     (				tr.AbstractTransform       ):


							'''simple docstring'''





							def __init__(	self							:  List[Any],  lowerCamelCase							:  str = " "					)-> List[str]:
										lowerCamelCase__		:		List[str]		     =sentence_delimiter



							def 		snake_case	(	self							:  Any,  lowerCamelCase							:  str					)-> Optional[Any]:
										return list(lowerCamelCase					)




							def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  List[str]					)-> Tuple:
										lowerCamelCase__		:		Optional[int]		     =[]
										for sent_idx, sentence in enumerate(lowerCamelCase					):
													chars.extend(self.process_string(lowerCamelCase					)					)
													if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase					) - 1:
																chars.append(self.sentence_delimiter					)
										return chars





		_lowercase			:			Optional[int]										=						tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		_lowercase			:			List[str]										=						tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


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

_lowercase			:			List[Any]										=						"\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER'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\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

_lowercase			:			Dict										=						"\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"

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


					'''simple docstring'''





					def 		snake_case	(	self							:  Dict					)-> Optional[Any]:
								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 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict=False					)-> List[Any]:
								if concatenate_texts:
											return jiwer.compute_measures(
											    lowerCamelCase,  lowerCamelCase,  truth_transform=lowerCamelCase,  hypothesis_transform=lowerCamelCase,  )["wer"]

								lowerCamelCase__		:		Optional[Any]		     =0
								lowerCamelCase__		:		Union[str, Any]		     =0
								for prediction, reference in zip(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		int		     =jiwer.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




