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


__A							=1_6
__A							=3_2





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__ = 1_6       ):
				lowerCamelCase_         =     AutoTokenizer.from_pretrained("bert-base-cased"       )
				lowerCamelCase_         =     load_dataset("glue"      ,					"mrpc"       )

				def tokenize_function(lowerCamelCase__       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase_         =     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():
								lowerCamelCase_         =     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
				lowerCamelCase_         =     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.
								lowerCamelCase_         =     1_2_8 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_         =     1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_         =     8
								else:
												lowerCamelCase_         =     None

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

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

				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_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,					lowerCamelCase__       ) == "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"       )

				# 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=lowerCamelCase__       )
				def inner_training_loop(lowerCamelCase__       ):
								# 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(lowerCamelCase__       )

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

								# Instantiate optimizer
								lowerCamelCase_         =     AdamW(params=model.parameters()      ,					lr=lowerCamelCase__       )
								lowerCamelCase_      ,       lowerCamelCase_         =     get_dataloaders(lowerCamelCase__      ,					lowerCamelCase__       )

								# Instantiate scheduler
								lowerCamelCase_         =     get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase__      ,					num_warmup_steps=1_0_0      ,					num_training_steps=(len(lowerCamelCase__       ) * 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.
								lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_         =     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       )
																lowerCamelCase_         =     model(**lowerCamelCase__       )
																lowerCamelCase_         =     outputs.loss
																accelerator.backward(lowerCamelCase__       )
																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():
																				lowerCamelCase_         =     model(**lowerCamelCase__       )
																lowerCamelCase_         =     outputs.logits.argmax(dim=-1       )
																lowerCamelCase_      ,       lowerCamelCase_         =     accelerator.gather_for_metrics((predictions, batch["labels"])       )
																metric.add_batch(
																    predictions=lowerCamelCase__      ,					references=lowerCamelCase__      ,					)

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

    # 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_					(							):
				lowerCamelCase_         =     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."       )
				lowerCamelCase_         =     parser.parse_args()
				lowerCamelCase_         =     {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__      ,					lowerCamelCase__       )


if __name__ == "__main__":
			main()




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'MCTCTFeatureExtractor'
	lowerCAmelCase__   =					'AutoTokenizer'



	def __init__(       self		,    lowercase		,    lowercase					)							->			str:
					super().__init__(lowercase		,    lowercase					)
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False



	def __call__(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase		,    **lowercase					)

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."					)
									lowerCamelCase_         =     kwargs.pop("raw_speech"					)
					else:
									lowerCamelCase_         =     kwargs.pop("audio"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("sampling_rate"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("text"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."					)

					if audio is not None:
									lowerCamelCase_         =     self.feature_extractor(lowercase		,    *lowercase		,    sampling_rate=lowercase		,    **lowercase					)
					if text is not None:
									lowerCamelCase_         =     self.tokenizer(lowercase		,    **lowercase					)

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_         =     encodings["input_ids"]
									return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					return self.tokenizer.batch_decode(*lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase		,    **lowercase					)

					lowerCamelCase_         =     kwargs.pop("input_features"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("labels"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if input_features is not None:
									lowerCamelCase_         =     self.feature_extractor.pad(lowercase		,    *lowercase		,    **lowercase					)
					if labels is not None:
									lowerCamelCase_         =     self.tokenizer.pad(lowercase		,    **lowercase					)

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_         =     labels["input_ids"]
									return input_features



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					return self.tokenizer.decode(*lowercase		,    **lowercase					)



	@contextmanager
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call."					)
					lowerCamelCase_         =     True
					lowerCamelCase_         =     self.tokenizer
					yield
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False


