





import argparse
import os

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
# and perform 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)
#
# 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				:    Optional[Any]       =   16
__A				:    str       =   32





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ = 16 )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''bert-base-cased''' )
			UpperCAmelCase   		=				load_dataset('''glue'''			,			'''mrpc''' )

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

			def collate_fn(UpperCamelCase__ ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						UpperCAmelCase   		=				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":
									UpperCAmelCase   		=				16
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase   		=				8
						else:
									UpperCAmelCase   		=				None

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

			# Instantiate dataloaders.
			UpperCAmelCase   		=				DataLoader(
			    tokenized_datasets['''train''']			,			shuffle=UpperCamelCase__			,			collate_fn=UpperCamelCase__			,			batch_size=UpperCamelCase__ )
			UpperCAmelCase   		=				DataLoader(
			    tokenized_datasets['''validation''']			,			shuffle=UpperCamelCase__			,			collate_fn=UpperCamelCase__			,			batch_size=UpperCamelCase__ )

			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

				__A				:    List[str]       =   mocked_dataloaders  # noqa: F811





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				str:







			'''simple docstring'''

			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,			UpperCamelCase__ ) == "1":
						UpperCAmelCase   		=				2
			# New Code #
			UpperCAmelCase   		=				int(args.gradient_accumulation_steps )
			# Initialize accelerator
			UpperCAmelCase   		=				Accelerator(
			    cpu=args.cpu			,			mixed_precision=args.mixed_precision			,			gradient_accumulation_steps=UpperCamelCase__ )
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`''' )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase   		=				config['''lr''']
			UpperCAmelCase   		=				int(config['''num_epochs'''] )
			UpperCAmelCase   		=				int(config['''seed'''] )
			UpperCAmelCase   		=				int(config['''batch_size'''] )

			UpperCAmelCase   		=				evaluate.load('''glue'''			,			'''mrpc''' )

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

			# 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).
			UpperCAmelCase   		=				model.to(accelerator.device )

			# Instantiate optimizer
			UpperCAmelCase   		=				AdamW(params=model.parameters()			,			lr=UpperCamelCase__ )

			# Instantiate scheduler
			UpperCAmelCase   		=				get_linear_schedule_with_warmup(
			    optimizer=UpperCamelCase__			,			num_warmup_steps=100			,			num_training_steps=(len(UpperCamelCase__ ) * 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.
			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				accelerator.prepare(
			    UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )

			# Now we train the model
			for epoch in range(UpperCamelCase__ ):
						model.train()
						for step, batch in enumerate(UpperCamelCase__ ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device )
									# New code #
									# We use the new `accumulate` context manager to perform gradient accumulation
									# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
									with accelerator.accumulate(UpperCamelCase__ ):
												UpperCAmelCase   		=				model(**UpperCamelCase__ )
												UpperCAmelCase   		=				output.loss
												accelerator.backward(UpperCamelCase__ )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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





def 						__SCREAMING_SNAKE_CASE  (				)  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				argparse.ArgumentParser(description='''Simple example of training script.''' )
			parser.add_argument(
			    '''--mixed_precision'''			,			type=UpperCamelCase__			,			default=UpperCamelCase__			,			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.'''			,			)
			# New Code #
			parser.add_argument(
			    '''--gradient_accumulation_steps'''			,			type=UpperCamelCase__			,			default=1			,			help='''The number of minibatches to be ran before gradients are accumulated.'''			,			)
			parser.add_argument('''--cpu'''			,			action='''store_true'''			,			help='''If passed, will train on the CPU.''' )
			UpperCAmelCase   		=				parser.parse_args()
			UpperCAmelCase   		=				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(UpperCamelCase__			,			UpperCamelCase__ )


if __name__ == "__main__":
				main()










from typing import TYPE_CHECKING

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


__A				:    Union[str, Any]       =   {
    "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:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "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

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



