



'''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
#
########################################################################


lowerCamelCase							:List[str]       = 1_6
lowerCamelCase							:Union[str, Any]       = 3_2



def      a      (			lowerCamelCase__						,				lowerCamelCase__ = 16   ):
			'''simple docstring'''





			A_     :						List[Any]     =					AutoTokenizer.from_pretrained("""bert-base-cased"""   )
			A_     :						int     =					load_dataset("""glue"""						,				"""mrpc"""   )

			def tokenize_function(lowerCamelCase__   ):
						# max_length=None => use the model max length (it's actually the default)
						A_     :						Optional[int]     =					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():
						A_     :						Tuple     =					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
			A_     :						List[Any]     =					tokenized_datasets.rename_column("""label"""						,				"""labels"""   )

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

						return tokenizer.pad(
						    lowerCamelCase__						,				padding="""longest"""						,				max_length=lowerCamelCase__						,				pad_to_multiple_of=lowerCamelCase__						,				return_tensors="""pt"""						,				)

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

			return train_dataloader, eval_dataloader



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					Accelerator(cpu=args.cpu						,				mixed_precision=args.mixed_precision   )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A_     :						Optional[int]     =					config["""lr"""]
			A_     :						Optional[int]     =					int(config["""num_epochs"""]   )
			A_     :						str     =					int(config["""seed"""]   )
			A_     :						Any     =					int(config["""batch_size"""]   )

			A_     :						int     =					evaluate.load("""glue"""						,				"""mrpc"""   )

			# If the batch size is too big we use gradient accumulation
			A_     :						Any     =					1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						A_     :						List[Any]     =					batch_size // MAX_GPU_BATCH_SIZE
						A_     :						Tuple     =					MAX_GPU_BATCH_SIZE

			set_seed(lowerCamelCase__   )
			A_,   A_     :						List[str]     =					get_dataloaders(lowerCamelCase__						,				lowerCamelCase__   )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A_     :						Any     =					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).
			A_     :						List[Any]     =					model.to(accelerator.device   )
			# Instantiate optimizer
			A_     :						Tuple     =					AdamW(params=model.parameters()						,				lr=lowerCamelCase__   )

			# Instantiate scheduler
			A_     :						Optional[Any]     =					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.

			A_,   A_,   A_,   A_,   A_     :						List[str]     =					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   )
									A_     :						Tuple     =					model(**lowerCamelCase__   )
									A_     :						Union[str, Any]     =					outputs.loss
									A_     :						List[str]     =					loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__   )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						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():
												A_     :						List[str]     =					model(**lowerCamelCase__   )
									A_     :						Optional[int]     =					outputs.logits.argmax(dim=-1   )
									A_,   A_     :						List[Any]     =					accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
									metric.add_batch(
									    predictions=lowerCamelCase__						,				references=lowerCamelCase__						,				)

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



def      a      (			):
			'''simple docstring'''





			A_     :						str     =					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."""   )
			A_     :						Union[str, Any]     =					parser.parse_args()
			A_     :						Dict     =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__						,				lowerCamelCase__   )


if __name__ == "__main__":
						main()




'''simple docstring'''




lowerCamelCase							:Any       = {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowerCamelCase							:Any       = {value: key for key, value in MORSE_CODE_DICT.items()}



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(MORSE_CODE_DICT[char] for char in message.upper()   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return "".join(REVERSE_DICT[char] for char in message.split()   )



def      a      (			):
			'''simple docstring'''





			A_     :						List[str]     =					"""Morse code here!"""
			print(lowerCamelCase__   )
			A_     :						str     =					encrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )
			A_     :						Dict     =					decrypt(lowerCamelCase__   )
			print(lowerCamelCase__   )


if __name__ == "__main__":
						main()