



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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowerCAmelCase__		:					Dict							=16
lowerCAmelCase__		:					List[Any]							=32






def      __lowercase       (			a__							,       a__ = 16       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('bert-base-cased'       )
							__SCREAMING_SNAKE_CASE									= load_dataset('glue'							,       'mrpc'       )

							def tokenize_function(a__       ):
														# max_length=None => use the model max length (it's actually the default)
														__SCREAMING_SNAKE_CASE									= 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():
														__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= 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.
														__SCREAMING_SNAKE_CASE									= 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":
																					__SCREAMING_SNAKE_CASE									= 16
														elif accelerator.mixed_precision != "no":
																					__SCREAMING_SNAKE_CASE									= 8
														else:
																					__SCREAMING_SNAKE_CASE									= None

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

							# Instantiate dataloaders.
							__SCREAMING_SNAKE_CASE									= DataLoader(
							    tokenized_datasets['train']							,       shuffle=__lowerCAmelCase							,       collate_fn=__lowerCAmelCase							,       batch_size=__lowerCAmelCase       )
							__SCREAMING_SNAKE_CASE									= 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

			lowerCAmelCase__		:					Optional[Any]							=mocked_dataloaders  # noqa: F811






def      __lowercase       (			a__							,       a__       )    ->			List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       __lowerCAmelCase       ) == "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'       )

							# If the batch size is too big we use gradient accumulation
							__SCREAMING_SNAKE_CASE									= 1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														__SCREAMING_SNAKE_CASE									= batch_size // MAX_GPU_BATCH_SIZE
														__SCREAMING_SNAKE_CASE									= MAX_GPU_BATCH_SIZE

							set_seed(__lowerCAmelCase       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= get_dataloaders(__lowerCAmelCase							,       __lowerCAmelCase       )
							# 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=__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).
							__SCREAMING_SNAKE_CASE									= model.to(accelerator.device       )

							# Instantiate optimizer
							__SCREAMING_SNAKE_CASE									= AdamW(params=model.parameters()							,       lr=__lowerCAmelCase       )

							# Instantiate scheduler
							__SCREAMING_SNAKE_CASE									= get_linear_schedule_with_warmup(
							    optimizer=__lowerCAmelCase							,       num_warmup_steps=1_00							,       num_training_steps=(len(__lowerCAmelCase       ) * 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.
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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       )
																					__SCREAMING_SNAKE_CASE									= model(**__lowerCAmelCase       )
																					__SCREAMING_SNAKE_CASE									= outputs.loss
																					__SCREAMING_SNAKE_CASE									= loss / gradient_accumulation_steps
																					accelerator.backward(__lowerCAmelCase       )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														__SCREAMING_SNAKE_CASE									= 0
														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():
																												__SCREAMING_SNAKE_CASE									= model(**__lowerCAmelCase       )
																					__SCREAMING_SNAKE_CASE									= outputs.logits.argmax(dim=-1       )
																					__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= accelerator.gather((predictions, batch['labels'])       )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(__lowerCAmelCase       ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			__SCREAMING_SNAKE_CASE									= predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																																			__SCREAMING_SNAKE_CASE									= references[: len(eval_dataloader.dataset       ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=__lowerCAmelCase							,       references=__lowerCAmelCase							,       )

														__SCREAMING_SNAKE_CASE									= metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""							,       __lowerCAmelCase       )






def      __lowercase       (			)    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= 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.'       )
							__SCREAMING_SNAKE_CASE									= parser.parse_args()
							__SCREAMING_SNAKE_CASE									= {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(__lowerCAmelCase							,       __lowerCAmelCase       )


if __name__ == "__main__":
			main()











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= vocab_size

													if (
													    (len(self.conv_stride		) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
													    or (len(self.conv_dim		) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    'Configuration for convolutional layers is incorrect.'
																				    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
																				    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
																				    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													__SCREAMING_SNAKE_CASE									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return functools.reduce(operator.mul				,							self.conv_stride				,							1		)






