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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Accelerator		,   SCREAMING_SNAKE_CASE_	:	int = 16       )  ->     str:
						_lowercase					       =					AutoTokenizer.from_pretrained("""bert-base-cased"""       )
						_lowercase					       =					load_dataset("""glue"""		,   """mrpc"""       )

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

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

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

						# Instantiate dataloaders.
						_lowercase					       =					DataLoader(
						    tokenized_datasets["""train"""]		,   shuffle=SCREAMING_SNAKE_CASE_		,   collate_fn=SCREAMING_SNAKE_CASE_		,   batch_size=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					DataLoader(
						    tokenized_datasets["""validation"""]		,   shuffle=SCREAMING_SNAKE_CASE_		,   collate_fn=SCREAMING_SNAKE_CASE_		,   batch_size=SCREAMING_SNAKE_CASE_       )

						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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,   SCREAMING_SNAKE_CASE_       ) == "1":
												_lowercase					       =					2
						# Initialize accelerator
						_lowercase					       =					Accelerator(cpu=args.cpu		,   mixed_precision=args.mixed_precision       )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_lowercase					       =					config["""lr"""]
						_lowercase					       =					int(config["""num_epochs"""]       )
						_lowercase					       =					int(config["""seed"""]       )
						_lowercase					       =					int(config["""batch_size"""]       )

						_lowercase					       =					evaluate.load("""glue"""		,   """mrpc"""       )

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

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

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

						# Instantiate optimizer
						_lowercase					       =					AdamW(params=model.parameters()		,   lr=SCREAMING_SNAKE_CASE_       )

						# Instantiate scheduler
						_lowercase					       =					get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE_		,   num_warmup_steps=1_00		,   num_training_steps=(len(SCREAMING_SNAKE_CASE_       ) * 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.
						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase					       =					accelerator.prepare(
						    SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

						# Now we train the model
						for epoch in range(SCREAMING_SNAKE_CASE_       ):
												model.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device       )
																		_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					outputs.loss
																		_lowercase					       =					loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE_       )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												_lowercase					       =					0
												for step, batch in enumerate(SCREAMING_SNAKE_CASE_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device       )
																		with torch.no_grad():
																								_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					outputs.logits.argmax(dim=-1       )
																		_lowercase       ,						_lowercase					       =					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(SCREAMING_SNAKE_CASE_       ) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														_lowercase					       =					predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																														_lowercase					       =					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=SCREAMING_SNAKE_CASE_		,   references=SCREAMING_SNAKE_CASE_		,   )

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




def 	UpperCamelCase__  (     )  ->     Any:
						_lowercase					       =					argparse.ArgumentParser(description="""Simple example of training script."""       )
						parser.add_argument(
						    """--mixed_precision"""		,   type=SCREAMING_SNAKE_CASE_		,   default=SCREAMING_SNAKE_CASE_		,   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."""       )
						_lowercase					       =					parser.parse_args()
						_lowercase					       =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		main()

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# bit count represents no. of bits in the gray code
						if bit_count < 0:
												raise ValueError("""The given input must be positive"""       )

						# get the generated string sequence
						_lowercase					       =					gray_code_sequence_string(SCREAMING_SNAKE_CASE_       )
						#
						# convert them to integers
						for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_lowercase					       =					int(sequence[i]		,   2       )

						return sequence




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# The approach is a recursive one
						# Base case achieved when either n = 0 or n=1
						if bit_count == 0:
												return ["0"]

						if bit_count == 1:
												return ["0", "1"]

						_lowercase					       =					1 << bit_count  # defines the length of the sequence
						# 1<< n is equivalent to 2^n

						# recursive answer will generate answer for n-1 bits
						_lowercase					       =					gray_code_sequence_string(bit_count - 1       )

						_lowercase					       =					[]

						# append 0 to first half of the smaller sequence generated
						for i in range(seq_len // 2       ):
												_lowercase					       =					"""0""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						# append 1 to second half ... start from the end of the list
						for i in reversed(range(seq_len // 2       )       ):
												_lowercase					       =					"""1""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()