





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


_SCREAMING_SNAKE_CASE							=						16
_SCREAMING_SNAKE_CASE							=						32


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a = 16      ):
	snake_case_						:	int      =		AutoTokenizer.from_pretrained('bert-base-cased'      )
	snake_case_						:	Optional[Any]      =		load_dataset('glue'      ,       'mrpc'      )

	def tokenize_function(__a      ):
		# max_length=None => use the model max length (it's actually the default)
		snake_case_						:	Tuple      =		tokenizer(examples['sentence1']      ,       examples['sentence2']      ,       truncation=snake_case__      ,       max_length=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_						:	Optional[int]      =		datasets.map(
		    snake_case__      ,       batched=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(__a      ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		snake_case_						:	Tuple      =		1_28 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_						:	List[Any]      =		16
		elif accelerator.mixed_precision != "no":
			snake_case_						:	Dict      =		8
		else:
			snake_case_						:	Tuple      =		None

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

	# Instantiate dataloaders.
	snake_case_						:	Optional[int]      =		DataLoader(
	    tokenized_datasets['train']      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__      ,       drop_last=snake_case__      )
	snake_case_						:	int      =		DataLoader(
	    tokenized_datasets['validation']      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__      ,       drop_last=(accelerator.mixed_precision == 'fp8')      ,       )

	return train_dataloader, eval_dataloader


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ):
	snake_case_						:	Dict      =		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_						:	Dict      =		int(config['num_epochs']      )
	snake_case_						:	List[str]      =		int(config['seed']      )
	snake_case_						:	Optional[int]      =		int(config['batch_size']      )

	snake_case_						:	Tuple      =		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_						:	str      =		batch_size // MAX_GPU_BATCH_SIZE
		snake_case_						:	int      =		MAX_GPU_BATCH_SIZE

	set_seed(snake_case__      )
	snake_case_						:	Optional[Any]      =		get_dataloaders(snake_case__      ,       snake_case__      )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_						:	List[str]      =		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,       return_dict=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_						:	int      =		model.to(accelerator.device      )
	# Instantiate optimizer
	snake_case_						:	List[Any]      =		AdamW(params=model.parameters()      ,       lr=snake_case__      )

	# Instantiate scheduler
	snake_case_						:	Any      =		get_linear_schedule_with_warmup(
	    optimizer=snake_case__      ,       num_warmup_steps=1_00      ,       num_training_steps=(len(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_						:	Tuple      =		accelerator.prepare(
	    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      )

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

		model.eval()
		for step, batch in enumerate(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_						:	str      =		model(**snake_case__      )
			snake_case_						:	str      =		outputs.logits.argmax(dim=-1      )
			snake_case_						:	List[str]      =		accelerator.gather_for_metrics((predictions, batch['labels'])      )
			metric.add_batch(
			    predictions=snake_case__      ,       references=snake_case__      ,       )

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


def 							SCREAMING_SNAKE_CASE__  (			):
	snake_case_						:	str      =		argparse.ArgumentParser(description='Simple example of training script.'      )
	parser.add_argument(
	    '--mixed_precision'      ,       type=snake_case__      ,       default=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.'      )
	snake_case_						:	int      =		parser.parse_args()
	snake_case_						:	Union[str, Any]      =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(snake_case__      ,       snake_case__      )


if __name__ == "__main__":
						main()



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




