

'''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
# and perform gradient accumulation
#
# 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 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__										= 16
lowerCAmelCase__										= 32







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1_6  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Tuple       	=					AutoTokenizer.from_pretrained('bert-base-cased'  )
	UpperCAmelCase_		:      int       	=					load_dataset('glue'   ,'mrpc'  )

	def tokenize_function(UpperCamelCase  ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_		:      Optional[Any]       	=					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():
		UpperCAmelCase_		:      Tuple       	=					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
	UpperCAmelCase_		:      Dict       	=					tokenized_datasets.rename_column('label'   ,'labels'  )

	def collate_fn(UpperCamelCase  ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_		:      Union[str, Any]       	=					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":
			UpperCAmelCase_		:      Optional[Any]       	=					1_6
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_		:      Dict       	=					8
		else:
			UpperCAmelCase_		:      Union[str, Any]       	=					None

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

	# Instantiate dataloaders.
	UpperCAmelCase_		:      Union[str, Any]       	=					DataLoader(
	    tokenized_datasets['train']   ,shuffle=_UpperCamelCase   ,collate_fn=_UpperCamelCase   ,batch_size=_UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					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

						lowerCAmelCase__										= mocked_dataloaders  # noqa: F811







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:


	if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,_UpperCamelCase  ) == "1":
		UpperCAmelCase_		:      Dict       	=					2
	# New Code #
	UpperCAmelCase_		:      Union[str, Any]       	=					int(args.gradient_accumulation_steps  )
	# Initialize accelerator
	UpperCAmelCase_		:      int       	=					Accelerator(
	    cpu=args.cpu   ,mixed_precision=args.mixed_precision   ,gradient_accumulation_steps=_UpperCamelCase  )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'  )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_		:      Any       	=					config['lr']
	UpperCAmelCase_		:      Union[str, Any]       	=					int(config['num_epochs']  )
	UpperCAmelCase_		:      List[str]       	=					int(config['seed']  )
	UpperCAmelCase_		:      Any       	=					int(config['batch_size']  )

	UpperCAmelCase_		:      Dict       	=					evaluate.load('glue'   ,'mrpc'  )

	set_seed(_UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					get_dataloaders(_UpperCamelCase   ,_UpperCamelCase  )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_		:      Dict       	=					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).
	UpperCAmelCase_		:      Optional[Any]       	=					model.to(accelerator.device  )

	# Instantiate optimizer
	UpperCAmelCase_		:      Optional[Any]       	=					AdamW(params=model.parameters()   ,lr=_UpperCamelCase  )

	# Instantiate scheduler
	UpperCAmelCase_		:      Any       	=					get_linear_schedule_with_warmup(
	    optimizer=_UpperCamelCase   ,num_warmup_steps=1_0_0   ,num_training_steps=(len(_UpperCamelCase  ) * 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.
	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					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  )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(_UpperCamelCase  ):
				UpperCAmelCase_		:      Optional[Any]       	=					model(**_UpperCamelCase  )
				UpperCAmelCase_		:      Optional[int]       	=					output.loss
				accelerator.backward(_UpperCamelCase  )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		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():
				UpperCAmelCase_		:      str       	=					model(**_UpperCamelCase  )
			UpperCAmelCase_		:      Optional[Any]       	=					outputs.logits.argmax(dim=-1  )
			UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					accelerator.gather_for_metrics((predictions, batch['labels'])  )
			metric.add_batch(
			    predictions=_UpperCamelCase   ,references=_UpperCamelCase   ,)

		UpperCAmelCase_		:      Optional[Any]       	=					metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""   ,_UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  )    ->    Optional[int]:
	UpperCAmelCase_		:      Optional[int]       	=					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.'   ,)
	# New Code #
	parser.add_argument(
	    '--gradient_accumulation_steps'   ,type=_UpperCamelCase   ,default=1   ,help='The number of minibatches to be ran before gradients are accumulated.'   ,)
	parser.add_argument('--cpu'   ,action='store_true'   ,help='If passed, will train on the CPU.'  )
	UpperCAmelCase_		:      Dict       	=					parser.parse_args()
	UpperCAmelCase_		:      Optional[int]       	=					{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(_UpperCamelCase   ,_UpperCamelCase  )


if __name__ == "__main__":
						main()









'''simple docstring'''


import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
						import bitsandbytes as bnb
						import torch
						import torch.nn as nn

						from ..pytorch_utils import ConvaD

if is_accelerate_available():
						from accelerate import init_empty_weights
						from accelerate.utils import find_tied_parameters

lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Any:
	# Recurse if needed
	if "." in tensor_name:
		UpperCAmelCase_		:      Tuple       	=					tensor_name.split('.'  )
		for split in splits[:-1]:
			UpperCAmelCase_		:      Optional[int]       	=					getattr(UpperCamelCase   ,UpperCamelCase  )
			if new_module is None:
				raise ValueError(f"""{module} has no attribute {split}."""  )
			UpperCAmelCase_		:      Union[str, Any]       	=					new_module
		UpperCAmelCase_		:      int       	=					splits[-1]

	if tensor_name not in module._parameters and tensor_name not in module._buffers:
		raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}."""  )
	UpperCAmelCase_		:      Tuple       	=					tensor_name in module._buffers
	UpperCAmelCase_		:      Any       	=					getattr(UpperCamelCase   ,UpperCamelCase  )

	if old_value.device == torch.device('meta'  ) and device not in ["meta", torch.device('meta'  )] and value is None:
		raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""  )

	UpperCAmelCase_		:      Any       	=					False
	UpperCAmelCase_		:      Any       	=					False
	if is_buffer or not is_bitsandbytes_available():
		UpperCAmelCase_		:      Dict       	=					False
		UpperCAmelCase_		:      Tuple       	=					False
	else:
		UpperCAmelCase_		:      List[str]       	=					hasattr(bnb.nn   ,'Params4bit'  ) and isinstance(module._parameters[tensor_name]   ,bnb.nn.Paramsabit  )
		UpperCAmelCase_		:      str       	=					isinstance(module._parameters[tensor_name]   ,bnb.nn.IntaParams  )

	if is_abit or is_abit:
		UpperCAmelCase_		:      Union[str, Any]       	=					module._parameters[tensor_name]
		if param.device.type != "cuda":
			if value is None:
				UpperCAmelCase_		:      List[str]       	=					old_value.to(UpperCamelCase  )
			elif isinstance(UpperCamelCase   ,torch.Tensor  ):
				UpperCAmelCase_		:      Any       	=					value.to('cpu'  )
				if value.dtype == torch.inta:
					UpperCAmelCase_		:      Dict       	=					version.parse(importlib.metadata.version('bitsandbytes'  )  ) > version.parse(
					    '0.37.2'  )
					if not is_abit_serializable:
						raise ValueError(
						    'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
						    'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'  )
			else:
				UpperCAmelCase_		:      List[Any]       	=					torch.tensor(UpperCamelCase   ,device='cpu'  )

			# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
			# Since weights are saved in the correct "orientation", we skip transposing when loading.
			if issubclass(module.source_cls   ,UpperCamelCase  ) and fpaa_statistics is None:
				UpperCAmelCase_		:      Dict       	=					new_value.T

			UpperCAmelCase_		:      Tuple       	=					old_value.__dict__
			if is_abit:
				UpperCAmelCase_		:      Any       	=					bnb.nn.IntaParams(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )
			elif is_abit:
				UpperCAmelCase_		:      Optional[Any]       	=					bnb.nn.Paramsabit(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					new_value
			if fpaa_statistics is not None:
				setattr(module.weight   ,'SCB'   ,fpaa_statistics.to(UpperCamelCase  )  )

	else:
		if value is None:
			UpperCAmelCase_		:      str       	=					old_value.to(UpperCamelCase  )
		elif isinstance(UpperCamelCase   ,torch.Tensor  ):
			UpperCAmelCase_		:      Tuple       	=					value.to(UpperCamelCase  )
		else:
			UpperCAmelCase_		:      str       	=					torch.tensor(UpperCamelCase   ,device=UpperCamelCase  )

		if is_buffer:
			UpperCAmelCase_		:      Dict       	=					new_value
		else:
			UpperCAmelCase_		:      Optional[int]       	=					nn.Parameter(UpperCamelCase   ,requires_grad=old_value.requires_grad  )
			UpperCAmelCase_		:      Optional[int]       	=					new_value







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=False  )    ->    Tuple:
	for name, module in model.named_children():
		if current_key_name is None:
			UpperCAmelCase_		:      Optional[int]       	=					[]
		current_key_name.append(UpperCamelCase  )

		if (isinstance(UpperCamelCase   ,nn.Linear  ) or isinstance(UpperCamelCase   ,UpperCamelCase  )) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			if not any(key in '.'.join(UpperCamelCase  ) for key in modules_to_not_convert  ):
				with init_empty_weights():
					if isinstance(UpperCamelCase   ,UpperCamelCase  ):
						UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					module.weight.shape
					else:
						UpperCAmelCase_		:      Dict       	=					module.in_features
						UpperCAmelCase_		:      Any       	=					module.out_features

					if quantization_config.quantization_method() == "llm_int8":
						UpperCAmelCase_		:      Optional[int]       	=					bnb.nn.LinearabitLt(
						    UpperCamelCase   ,UpperCamelCase   ,module.bias is not None   ,has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight   ,threshold=quantization_config.llm_inta_threshold   ,)
						UpperCAmelCase_		:      List[Any]       	=					True
					else:
						if (
						    quantization_config.llm_inta_skip_modules is not None
						    and name in quantization_config.llm_inta_skip_modules
						):
							pass
						else:
							UpperCAmelCase_		:      Union[str, Any]       	=					bnb.nn.Linearabit(
							    UpperCamelCase   ,UpperCamelCase   ,module.bias is not None   ,quantization_config.bnb_abit_compute_dtype   ,compress_statistics=quantization_config.bnb_abit_use_double_quant   ,quant_type=quantization_config.bnb_abit_quant_type   ,)
							UpperCAmelCase_		:      Dict       	=					True
                    # Store the module class in case we need to transpose the weight later
					UpperCAmelCase_		:      List[str]       	=					type(UpperCamelCase  )
					# Force requires grad to False to avoid unexpected errors
					model._modules[name].requires_grad_(UpperCamelCase  )
		if len(list(module.children()  )  ) > 0:
			UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					_replace_with_bnb_linear(
			    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,has_been_replaced=UpperCamelCase   ,)
		# Remove the last key for recursion
		current_key_name.pop(-1  )
	return model, has_been_replaced







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Optional[int]:
	UpperCAmelCase_		:      Union[str, Any]       	=					['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
	UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					_replace_with_bnb_linear(
	    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

	if not has_been_replaced:
		logger.warning(
		    'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
		    ' Please double check your model architecture, or submit an issue on github if you think this is'
		    ' a bug.'  )

	return model







def 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    Tuple:
	warnings.warn(
	    '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'   ,UpperCamelCase   ,)
	return replace_with_bnb_linear(*UpperCamelCase   ,**UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    List[Any]:
	warnings.warn(
	    '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'   ,UpperCamelCase   ,)
	return set_module_quantized_tensor_to_device(*UpperCamelCase   ,**UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      Union[str, Any]       	=					deepcopy(UpperCamelCase  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
	tied_model.tie_weights()

	UpperCAmelCase_		:      Tuple       	=					find_tied_parameters(UpperCamelCase  )
	# For compatibility with Accelerate < 0.18
	if isinstance(UpperCamelCase   ,UpperCamelCase  ):
		UpperCAmelCase_		:      Dict       	=					sum(list(tied_params.values()  )   ,[]  ) + list(tied_params.keys()  )
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					sum(UpperCamelCase   ,[]  )
	UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) > 0

	# Check if it is a base model
	UpperCAmelCase_		:      List[Any]       	=					not hasattr(UpperCamelCase   ,model.base_model_prefix  )

	# Ignore this for base models (BertModel, GPT2Model, etc.)
	if (not has_tied_params) and is_base_model:
		return []

	# otherwise they have an attached head
	UpperCAmelCase_		:      str       	=					list(model.named_children()  )
	UpperCAmelCase_		:      Optional[int]       	=					[list_modules[-1][0]]

	# add last module together with tied weights
	UpperCAmelCase_		:      Any       	=					set(UpperCamelCase  ) - set(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					list(set(UpperCamelCase  )  ) + list(UpperCamelCase  )

	# remove ".weight" from the keys
	UpperCAmelCase_		:      Optional[int]       	=					['.weight', '.bias']
	UpperCAmelCase_		:      Tuple       	=					[]
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				UpperCAmelCase_		:      Union[str, Any]       	=					name.replace(UpperCamelCase   ,''  )
		filtered_module_names.append(UpperCamelCase  )

	return filtered_module_names






