




'''simple docstring'''







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 the checkpointing capability,
# 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
#
########################################################################

_lowerCamelCase				:		Optional[int]				      =    16
_lowerCamelCase				:		Optional[Any]				      =    32






def 						_lowercase						(      lowercase_		,				lowercase_ = 16    ):



		'''simple docstring'''







		_snake_case      			=			AutoTokenizer.from_pretrained("bert-base-cased"    )
		_snake_case      			=			load_dataset("glue"		,				"mrpc"    )

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

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

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

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

		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

						_lowerCamelCase				:		List[str]				      =    mocked_dataloaders  # noqa: F811






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

		# New Code #
		# Parse out whether we are saving every epoch or after a certain number of batches
		if hasattr(args.checkpointing_steps		,				"isdigit"    ):
				if args.checkpointing_steps == "epoch":
						_snake_case      			=			args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						_snake_case      			=			int(args.checkpointing_steps    )
				else:
						raise ValueError(
						    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
		else:
				_snake_case      			=			None

		set_seed(lowercase_    )

		_snake_case			,       _snake_case      			=			get_dataloaders(lowercase_		,				lowercase_    )
		_snake_case      			=			evaluate.load("glue"		,				"mrpc"    )

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

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

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

		# Instantiate optimizer
		_snake_case      			=			AdamW(params=model.parameters()		,				lr=lowercase_    )

		# Instantiate scheduler
		_snake_case      			=			get_linear_schedule_with_warmup(
		    optimizer=lowercase_		,				num_warmup_steps=100		,				num_training_steps=(len(lowercase_    ) * 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.
		_snake_case			,       _snake_case			,       _snake_case			,       _snake_case			,       _snake_case      			=			accelerator.prepare(
		    lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_    )

		# New Code #
		# We need to keep track of how many total steps we have iterated over
		_snake_case      			=			0
		# We also need to keep track of the stating epoch so files are named properly
		_snake_case      			=			0

		# We need to load the checkpoint back in before training here with `load_state`
		# The total number of epochs is adjusted based on where the state is being loaded from,
		# as we assume continuation of the same training script
		if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
						accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
						accelerator.load_state(args.resume_from_checkpoint    )
						_snake_case      			=			os.path.basename(args.resume_from_checkpoint    )
				else:
						# Get the most recent checkpoint
						_snake_case      			=			[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
						dirs.sort(key=os.path.getctime    )
						_snake_case      			=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				_snake_case      			=			os.path.splitext(lowercase_    )[0]

				if "epoch" in training_difference:
						_snake_case      			=			int(training_difference.replace("epoch_"		,				""    )    ) + 1
						_snake_case      			=			None
				else:
						_snake_case      			=			int(training_difference.replace("step_"		,				""    )    )
						_snake_case      			=			resume_step // len(lowercase_    )
						resume_step -= starting_epoch * len(lowercase_    )

    # Now we train the model
		for epoch in range(lowercase_		,				lowercase_    ):
				model.train()
				# New Code #
				if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
						# We need to skip steps until we reach the resumed step
						_snake_case      			=			accelerator.skip_first_batches(lowercase_		,				lowercase_    )
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						_snake_case      			=			train_dataloader
				for step, batch in enumerate(lowercase_    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						_snake_case      			=			model(**lowercase_    )
						_snake_case      			=			outputs.loss
						_snake_case      			=			loss / gradient_accumulation_steps
						accelerator.backward(lowercase_    )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
						# New Code #
						overall_step += 1

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `step_{overall_step}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if isinstance(lowercase_		,				lowercase_    ):
								_snake_case      			=			f'''step_{overall_step}'''
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												_snake_case      			=			os.path.join(args.output_dir		,				lowercase_    )
										accelerator.save_state(lowercase_    )

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

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

				# New Code #
				# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
				# These are saved to folders named `epoch_{epoch}`
				# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
				# If mixed precision was used, will also save a "scalar.bin" file
				if checkpointing_steps == "epoch":
						_snake_case      			=			f'''epoch_{epoch}'''
						if args.output_dir is not None:
								_snake_case      			=			os.path.join(args.output_dir		,				lowercase_    )
						accelerator.save_state(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			argparse.ArgumentParser(description="Simple example of training script."    )
		parser.add_argument(
		    "--mixed_precision"		,				type=lowercase_		,				default=lowercase_		,				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."    )
		parser.add_argument(
		    "--checkpointing_steps"		,				type=lowercase_		,				default=lowercase_		,				help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		,				)
		parser.add_argument(
		    "--output_dir"		,				type=lowercase_		,				default="."		,				help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,				)
		parser.add_argument(
		    "--resume_from_checkpoint"		,				type=lowercase_		,				default=lowercase_		,				help="If the training should continue from a checkpoint folder."		,				)
		_snake_case      			=			parser.parse_args()
		_snake_case      			=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(lowercase_		,				lowercase_    )


if __name__ == "__main__":
						main()











'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase				:		str				      =    'src/transformers'

# Matches is_xxx_available()
_lowerCamelCase				:		str				      =    re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_lowerCamelCase				:		Union[str, Any]				      =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_lowerCamelCase				:		Dict				      =    re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_lowerCamelCase				:		int				      =    '\n{0} = None\n'

_lowerCamelCase				:		List[Any]				      =    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_lowerCamelCase				:		Any				      =    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if _re_test_backend.search(lowercase_    ) is None:
				return None
		_snake_case      			=			[b[0] for b in _re_backend.findall(lowercase_    )]
		backends.sort()
		return "_and_".join(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		with open(os.path.join(lowercase_		,				"__init__.py"    )		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
				_snake_case      			=			f.readlines()

		# Get to the point we do the actual imports for type checking
		_snake_case      			=			0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		_snake_case      			=			{}
		# Go through the end of the file
		while line_index < len(lowercase_    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_snake_case      			=			find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						_snake_case      			=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								_snake_case      			=			lines[line_index]
								_snake_case      			=			_re_single_line_import.search(lowercase_    )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "    )    )
								elif line.startswith(" " * 12    ):
										objects.append(line[12:-2]    )
								line_index += 1

						_snake_case      			=			objects
				else:
						line_index += 1

		return backend_specific_objects






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if name.isupper():
				return DUMMY_CONSTANT.format(lowercase_    )
		elif name.islower():
				return DUMMY_FUNCTION.format(lowercase_		,				lowercase_    )
		else:
				return DUMMY_CLASS.format(lowercase_		,				lowercase_    )






def 						_lowercase						(      lowercase_=None    ):



		'''simple docstring'''







		if backend_specific_objects is None:
				_snake_case      			=			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_snake_case      			=			{}

		for backend, objects in backend_specific_objects.items():
				_snake_case      			=			"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"    )    ) + "]"
				_snake_case      			=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(lowercase_		,				lowercase_    ) for o in objects]    )
				_snake_case      			=			dummy_file

		return dummy_files






def 						_lowercase						(      lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_snake_case      			=			{"torch": "pt"}

		# Locate actual dummy modules and read their content.
		_snake_case      			=			os.path.join(lowercase_		,				"utils"    )
		_snake_case      			=			{
		    backend: os.path.join(lowercase_		,				f'''dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py'''    )
		    for backend in dummy_files.keys()
		}

		_snake_case      			=			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(lowercase_    ):
						with open(lowercase_		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
								_snake_case      			=			f.read()
				else:
						_snake_case      			=			""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py as the main '''
								    "__init__ has new objects."    )
								with open(dummy_file_paths[backend]		,				"w"		,				encoding="utf-8"		,				newline="\n"    ) as f:
										f.write(dummy_files[backend]    )
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py. Run `make fix-copies` '''
								    "to fix this."    )


if __name__ == "__main__":
						_lowerCamelCase				:		int				      =    argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()

						check_dummies(args.fix_and_overwrite)





