




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


__A								=       16
__A								=       32







def      lowerCamelCase_						(			UpperCamelCase__							: Accelerator					,	UpperCamelCase__							: int = 16 )	->			Any:





				"""simple docstring"""


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

				def tokenize_function(UpperCamelCase__							: Union[str, Any] ):
								# max_length=None => use the model max length (it's actually the default)
								__lowerCamelCase									=       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():
								__lowerCamelCase									=       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
				__lowerCamelCase									=       tokenized_datasets.rename_column('label'					,	'labels' )

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

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

				# Instantiate dataloaders.
				__lowerCamelCase									=       DataLoader(
				    tokenized_datasets['train']					,	shuffle=UpperCamelCase__					,	collate_fn=UpperCamelCase__					,	batch_size=UpperCamelCase__ )
				__lowerCamelCase									=       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								=       mocked_dataloaders  # noqa: F811







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Union[str, Any] )	->			Any:





				"""simple docstring"""


				if os.environ.get('TESTING_MOCKED_DATALOADERS'					,	UpperCamelCase__ ) == "1":
								__lowerCamelCase									=       2
				# Initialize accelerator
				__lowerCamelCase									=       Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowerCamelCase									=       config['lr']
				__lowerCamelCase									=       int(config['num_epochs'] )
				__lowerCamelCase									=       int(config['seed'] )
				__lowerCamelCase									=       int(config['batch_size'] )

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

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

				set_seed(UpperCamelCase__ )
				__lowerCamelCase			,		__lowerCamelCase									=       get_dataloaders(UpperCamelCase__					,	UpperCamelCase__ )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowerCamelCase									=       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).
				__lowerCamelCase									=       model.to(accelerator.device )

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

				# Instantiate scheduler
				__lowerCamelCase									=       get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__					,	num_warmup_steps=100					,	num_training_steps=(len(UpperCamelCase__ ) * 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.
				__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       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 )
												__lowerCamelCase									=       model(**UpperCamelCase__ )
												__lowerCamelCase									=       outputs.loss
												__lowerCamelCase									=       loss / gradient_accumulation_steps
												accelerator.backward(UpperCamelCase__ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowerCamelCase									=       0
								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():
																__lowerCamelCase									=       model(**UpperCamelCase__ )
												__lowerCamelCase									=       outputs.logits.argmax(dim=-1 )
												__lowerCamelCase			,		__lowerCamelCase									=       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(UpperCamelCase__ ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowerCamelCase									=       predictions[: len(eval_dataloader.dataset ) - samples_seen]
																				__lowerCamelCase									=       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=UpperCamelCase__					,	references=UpperCamelCase__					,	)

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







def      lowerCamelCase_						(			)	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       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.'					,	)
				parser.add_argument('--cpu'					,	action='store_true'					,	help='If passed, will train on the CPU.' )
				__lowerCamelCase									=       parser.parse_args()
				__lowerCamelCase									=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(UpperCamelCase__					,	UpperCamelCase__ )


if __name__ == "__main__":
					main()








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


