


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case__			 =						16
snake_case__			 =						32


def 				lowerCamelCase__	(      a	:   Accelerator	,  a	:   int = 16	,  a	:   str = "bert-base-cased"				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Dict						  =    AutoTokenizer.from_pretrained(a				)
			a__ :Dict						  =    load_dataset("glue"	,  "mrpc"				)

			def tokenize_function(a	:   int				):
						# max_length=None => use the model max length (it's actually the default)
						a__ :Tuple						  =    tokenizer(examples["sentence1"]	,  examples["sentence2"]	,  truncation=a	,  max_length=a				)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			a__ :List[Any]						  =    datasets.map(
			    a	,  batched=a	,  remove_columns=["idx", "sentence1", "sentence2"]	,  load_from_cache_file=a				)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a__ :Union[str, Any]						  =    tokenized_datasets.rename_column("label"	,  "labels"				)

			def collate_fn(a	:   int				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(a	,  padding="max_length"	,  max_length=128	,  return_tensors="pt"				)
						return tokenizer.pad(a	,  padding="longest"	,  return_tensors="pt"				)

			# Instantiate dataloaders.
			a__ :Optional[int]						  =    DataLoader(
			    tokenized_datasets["train"]	,  shuffle=a	,  collate_fn=a	,  batch_size=a				)
			a__ :Union[str, Any]						  =    DataLoader(
			    tokenized_datasets["validation"]	,  shuffle=a	,  collate_fn=a	,  batch_size=a				)

			return train_dataloader, eval_dataloader


def 				lowerCamelCase__	(      a	:   Any	,  a	:   int	,  a	:   List[str]	,  a	:   Any				)    ->	Any:





			"""simple docstring"""
			model.eval()
			a__ :Any						  =    0
			for step, batch in enumerate(a				):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device				)
						with torch.no_grad():
									a__ :Optional[Any]						  =    model(**a				)
						a__ :List[str]						  =    outputs.logits.argmax(dim=-1				)
						# It is slightly faster to call this once, than multiple times
						a__ :List[str]						  =    accelerator.gather(
						    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
									if step == len(a				) - 1:
												a__ :Dict						  =    predictions[: len(eval_dataloader.dataset				) - samples_seen]
												a__ :Optional[Any]						  =    references[: len(eval_dataloader.dataset				) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=a	,  references=a	,  )

			a__ :Dict						  =    metric.compute()
			return eval_metric["accuracy"]


def 				lowerCamelCase__	(      a	:   Any	,  a	:   List[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :List[str]						  =    Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__ :Any						  =    config["lr"]
			a__ :List[Any]						  =    int(config["num_epochs"]				)
			a__ :List[Any]						  =    int(config["seed"]				)
			a__ :str						  =    int(config["batch_size"]				)
			a__ :Tuple						  =    args.model_name_or_path

			set_seed(a				)
			a__ :int						  =    get_dataloaders(a	,  a	,  a				)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__ :List[str]						  =    AutoModelForSequenceClassification.from_pretrained(a	,  return_dict=a				)

			# Instantiate optimizer
			a__ :Optional[Any]						  =    (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			a__ :Dict						  =    optimizer_cls(params=model.parameters()	,  lr=a				)

			if accelerator.state.deepspeed_plugin is not None:
						a__ :Optional[int]						  =    accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						a__ :int						  =    1
			a__ :Tuple						  =    (len(a				) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						a__ :List[str]						  =    get_linear_schedule_with_warmup(
						    optimizer=a	,  num_warmup_steps=0	,  num_training_steps=a	,  )
			else:
						a__ :Union[str, Any]						  =    DummyScheduler(a	,  total_num_steps=a	,  warmup_num_steps=0				)

			# 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.
			a__ :Optional[Any]						  =    accelerator.prepare(
			    a	,  a	,  a	,  a	,  a				)

			# We need to keep track of how many total steps we have iterated over
			a__ :Any						  =    0
			# We also need to keep track of the stating epoch so files are named properly
			a__ :int						  =    0
			a__ :int						  =    evaluate.load("glue"	,  "mrpc"				)
			a__ :Union[str, Any]						  =    num_epochs

			if args.partial_train_epoch is not None:
						a__ :List[str]						  =    args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint				)
						a__ :List[str]						  =    args.resume_from_checkpoint.split("epoch_"				)[1]
						a__ :Dict						  =    ""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						a__ :str						  =    int(a				) + 1
						a__ :Dict						  =    evaluation_loop(a	,  a	,  a	,  a				)
						accelerator.print("resumed checkpoint performance:"	,  a				)
						accelerator.print("resumed checkpoint's scheduler's lr:"	,  lr_scheduler.get_lr()[0]				)
						accelerator.print("resumed optimizers's lr:"	,  optimizer.param_groups[0]["lr"]				)
						with open(os.path.join(args.output_dir	,  F'''state_{starting_epoch-1}.json'''				)	,  "r"				) as f:
									a__ :Optional[int]						  =    json.load(a				)
									assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["lr"] == lr_scheduler.get_lr()[0]
									), "Scheduler learning rate mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
									), "Optimizer learning rate mismatch, loading from checkpoint failed"
									assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
									return

    # Now we train the model
			a__ :str						  =    {}
			for epoch in range(a	,  a				):
						model.train()
						for step, batch in enumerate(a				):
									a__ :Optional[int]						  =    model(**a				)
									a__ :str						  =    outputs.loss
									a__ :Tuple						  =    loss / gradient_accumulation_steps
									accelerator.backward(a				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						a__ :str						  =    F'''epoch_{epoch}'''
						a__ :Any						  =    os.path.join(args.output_dir	,  a				)
						accelerator.save_state(a				)
						a__ :Optional[int]						  =    evaluation_loop(a	,  a	,  a	,  a				)
						a__ :int						  =    accuracy
						a__ :Dict						  =    lr_scheduler.get_lr()[0]
						a__ :Optional[int]						  =    optimizer.param_groups[0]["lr"]
						a__ :int						  =    epoch
						a__ :Union[str, Any]						  =    overall_step
						accelerator.print(F'''epoch {epoch}:'''	,  a				)

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									with open(os.path.join(args.output_dir	,  F'''state_{epoch}.json'''				)	,  "w"				) as f:
												json.dump(a	,  a				)


def 				lowerCamelCase__	(      )    ->	int:





			"""simple docstring"""
			a__ :Optional[int]						  =    argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
			parser.add_argument(
			    "--model_name_or_path"	,  type=a	,  default="bert-base-cased"	,  help="Path to pretrained model or model identifier from huggingface.co/models."	,  required=a	,  )
			parser.add_argument(
			    "--output_dir"	,  type=a	,  default="."	,  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,  )
			parser.add_argument(
			    "--resume_from_checkpoint"	,  type=a	,  default=a	,  help="If the training should continue from a checkpoint folder."	,  )
			parser.add_argument(
			    "--partial_train_epoch"	,  type=a	,  default=a	,  help="If passed, the training will stop after this number of epochs."	,  )
			parser.add_argument(
			    "--num_epochs"	,  type=a	,  default=2	,  help="Number of train epochs."	,  )
			a__ :List[Any]						  =    parser.parse_args()
			a__ :int						  =    {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

			training_function(a	,  a				)


if __name__ == "__main__":
		main()






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    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
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






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


