
import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


snake_case :		Any							      =  16
snake_case :		Any							      =  32






def   lowerCAmelCase_						(					_snake_case      :  Accelerator      ,   _snake_case      :  int = 16   )				->		Tuple:







	'''simple docstring'''


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

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

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

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

	# Instantiate dataloaders.
	__magic_name__		:		int	   =     DataLoader(
	    tokenized_datasets["train"]      ,   shuffle=_snake_case      ,   collate_fn=_snake_case      ,   batch_size=_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     DataLoader(
	    tokenized_datasets["validation"]      ,   shuffle=_snake_case      ,   collate_fn=_snake_case      ,   batch_size=_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

				snake_case :		Tuple							      =  mocked_dataloaders  # noqa: F811






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  List[str]   )				->		Union[str, Any]:







	'''simple docstring'''


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

	__magic_name__		:		Optional[int]	   =     evaluate.load("glue"      ,   "mrpc"   )

	# New Code #
	# We now can define an inner training loop function. It should take a batch size as the only parameter,
	# and build the dataloaders in there.
	# It also gets our decorator
	@find_executable_batch_size(starting_batch_size=_snake_case   )
	def inner_training_loop(_snake_case      :  int   ):
		# And now just move everything below under this function
		# We need to bring in the Accelerator object from earlier
		nonlocal accelerator
		# And reset all of its attributes that could hold onto any memory:
		accelerator.free_memory()

		# Then we can declare the model, optimizer, and everything else:
		set_seed(_snake_case   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__magic_name__		:		Optional[int]	   =     AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,   return_dict=_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).
		__magic_name__		:		Optional[Any]	   =     model.to(accelerator.device   )

		# Instantiate optimizer
		__magic_name__		:		int	   =     AdamW(params=model.parameters()      ,   lr=_snake_case   )
		__magic_name__   ,    __magic_name__		:		int	   =     get_dataloaders(_snake_case      ,   _snake_case   )

		# Instantiate scheduler
		__magic_name__		:		Any	   =     get_linear_schedule_with_warmup(
		    optimizer=_snake_case      ,   num_warmup_steps=100      ,   num_training_steps=(len(_snake_case   ) * num_epochs)      ,   )

		# 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.
		__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		str	   =     accelerator.prepare(
		    _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )

		# Now we train the model
		for epoch in range(_snake_case   ):
			model.train()
			for step, batch in enumerate(_snake_case   ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device   )
				__magic_name__		:		Dict	   =     model(**_snake_case   )
				__magic_name__		:		List[str]	   =     outputs.loss
				accelerator.backward(_snake_case   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			model.eval()
			for step, batch in enumerate(_snake_case   ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device   )
				with torch.no_grad():
					__magic_name__		:		str	   =     model(**_snake_case   )
				__magic_name__		:		Tuple	   =     outputs.logits.argmax(dim=-1   )
				__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     accelerator.gather_for_metrics((predictions, batch["labels"])   )
				metric.add_batch(
				    predictions=_snake_case      ,   references=_snake_case      ,   )

			__magic_name__		:		str	   =     metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F'''epoch {epoch}:'''      ,   _snake_case   )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
	inner_training_loop()






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     argparse.ArgumentParser(description="Simple example of training script."   )
	parser.add_argument(
	    "--mixed_precision"      ,   type=_snake_case      ,   default=_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."   )
	__magic_name__		:		List[Any]	   =     parser.parse_args()
	__magic_name__		:		Tuple	   =     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_snake_case      ,   _snake_case   )


if __name__ == "__main__":
				main()


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
snake_case :		Dict							      =  "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de
