






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


_UpperCAmelCase			:		Optional[int]   				=    16
_UpperCAmelCase			:		Tuple   				=    32





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


				'''simple docstring'''




				lowerCAmelCase__      			=						AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				lowerCAmelCase__      			=						load_dataset('''glue'''     ,					'''mrpc'''       )

				def tokenize_function(lowercase__						:       Dict       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__      			=						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():
								lowerCAmelCase__      			=						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
				lowerCAmelCase__      			=						tokenized_datasets.rename_column('''label'''     ,					'''labels'''       )

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

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

				# Instantiate dataloaders.
				lowerCAmelCase__      			=						DataLoader(
				    tokenized_datasets['''train''']     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=lowercase__       )
				lowerCAmelCase__      			=						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

			_UpperCAmelCase			:		str   				=    mocked_dataloaders  # noqa: F811





def 	lowerCAmelCase_		(lowercase__						:       Dict     ,					lowercase__						:       int       )		->    Any:


				'''simple docstring'''




				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,					lowercase__       ) == "1":
								lowerCAmelCase__      			=						2
				# New Code #
				lowerCAmelCase__      			=						int(args.gradient_accumulation_steps       )
				lowerCAmelCase__      			=						int(args.local_sgd_steps       )
				# Initialize accelerator
				lowerCAmelCase__      			=						Accelerator(
				    cpu=args.cpu     ,					mixed_precision=args.mixed_precision     ,					gradient_accumulation_steps=lowercase__       )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
				# 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'''       )

				set_seed(lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						get_dataloaders(lowercase__     ,					lowercase__       )
				# 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=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).
				lowerCAmelCase__      			=						model.to(accelerator.device       )

				# Instantiate optimizer
				lowerCAmelCase__      			=						AdamW(params=model.parameters()     ,					lr=lowercase__       )

				# Instantiate scheduler
				lowerCAmelCase__      			=						get_linear_schedule_with_warmup(
				    optimizer=lowercase__     ,					num_warmup_steps=1_00     ,					num_training_steps=(len(lowercase__       ) * 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(
				    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

				# Now we train the model
				for epoch in range(lowercase__       ):
								model.train()
								with LocalSGD(
								    accelerator=lowercase__     ,					model=lowercase__     ,					local_sgd_steps=lowercase__     ,					enabled=local_sgd_steps is not None       ) as local_sgd:
												for step, batch in enumerate(lowercase__       ):
																# 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(lowercase__       ):
																				lowerCAmelCase__      			=						model(**lowercase__       )
																				lowerCAmelCase__      			=						output.loss
																				accelerator.backward(lowercase__       )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

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

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





def 	lowerCAmelCase_		()		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						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.'''     ,					)
				# New Code #
				parser.add_argument(
				    '''--gradient_accumulation_steps'''     ,					type=lowercase__     ,					default=1     ,					help='''The number of minibatches to be ran before gradients are accumulated.'''     ,					)
				parser.add_argument(
				    '''--local_sgd_steps'''     ,					type=lowercase__     ,					default=8     ,					help='''Number of local SGD steps or None to disable local SGD'''       )
				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''': 42, '''batch_size''': 16}
				training_function(lowercase__     ,					lowercase__       )


if __name__ == "__main__":
			main()













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''	)

											super().__init__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





