



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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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__		:					Any							=16
lowerCAmelCase__		:					Dict							=32






def      __lowercase       (			a__							,       a__ = 16       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('bert-base-cased'       )
							__SCREAMING_SNAKE_CASE									= load_dataset('glue'							,       'mrpc'       )

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

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

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

							# Instantiate dataloaders.
							__SCREAMING_SNAKE_CASE									= DataLoader(
							    tokenized_datasets['train']							,       shuffle=UpperCAmelCase_							,       collate_fn=UpperCAmelCase_							,       batch_size=UpperCAmelCase_       )
							__SCREAMING_SNAKE_CASE									= 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__		:					Optional[Any]							=mocked_dataloaders  # noqa: F811






def      __lowercase       (			a__							,       a__       )    ->			List[str]:





							if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       UpperCAmelCase_       ) == "1":
														__SCREAMING_SNAKE_CASE									= 2
							# Initialize accelerator
							__SCREAMING_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
							__SCREAMING_SNAKE_CASE									= config['lr']
							__SCREAMING_SNAKE_CASE									= int(config['num_epochs']       )
							__SCREAMING_SNAKE_CASE									= int(config['seed']       )
							__SCREAMING_SNAKE_CASE									= int(config['batch_size']       )

							__SCREAMING_SNAKE_CASE									= evaluate.load('glue'							,       'mrpc'       )

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

							set_seed(UpperCAmelCase_       )
							__SCREAMING_SNAKE_CASE									= get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_       )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__SCREAMING_SNAKE_CASE									= 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).
							__SCREAMING_SNAKE_CASE									= model.to(accelerator.device       )

							# Instantiate optimizer
							__SCREAMING_SNAKE_CASE									= AdamW(params=model.parameters()							,       lr=UpperCAmelCase_       )

							# Instantiate scheduler
							__SCREAMING_SNAKE_CASE									= get_linear_schedule_with_warmup(
							    optimizer=UpperCAmelCase_							,       num_warmup_steps=1_00							,       num_training_steps=(len(UpperCAmelCase_       ) * 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.
							__SCREAMING_SNAKE_CASE									= 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       )
																					__SCREAMING_SNAKE_CASE									= model(**UpperCAmelCase_       )
																					__SCREAMING_SNAKE_CASE									= outputs.loss
																					__SCREAMING_SNAKE_CASE									= loss / gradient_accumulation_steps
																					accelerator.backward(UpperCAmelCase_       )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														__SCREAMING_SNAKE_CASE									= 0
														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():
																												__SCREAMING_SNAKE_CASE									= model(**UpperCAmelCase_       )
																					__SCREAMING_SNAKE_CASE									= outputs.logits.argmax(dim=-1       )
																					__SCREAMING_SNAKE_CASE									= accelerator.gather((predictions, batch['labels'])       )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(UpperCAmelCase_       ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			__SCREAMING_SNAKE_CASE									= predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																																			__SCREAMING_SNAKE_CASE									= references[: len(eval_dataloader.dataset       ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=UpperCAmelCase_							,       references=UpperCAmelCase_							,       )

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






def      __lowercase       (			)    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= 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.'							,       )
							parser.add_argument('--cpu'							,       action='store_true'							,       help='If passed, will train on the CPU.'       )
							__SCREAMING_SNAKE_CASE									= parser.parse_args()
							__SCREAMING_SNAKE_CASE									= {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(UpperCAmelCase_							,       UpperCAmelCase_       )


if __name__ == "__main__":
			main()











from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''The output directory where the model will be written.'''}   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}	)







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

							# Use explicit specified encoder config
							if model_args.encoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_config_name       )
							# Use pretrained encoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path       )

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_config_name       )
							# Use pretrained decoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path       )

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							__SCREAMING_SNAKE_CASE									= True
							__SCREAMING_SNAKE_CASE									= True

							__SCREAMING_SNAKE_CASE									= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
							    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							,       encoder_config=a__							,       decoder_config=a__							,       )

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							__SCREAMING_SNAKE_CASE									= decoder_config.decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.bos_token_id
							if pad_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id

							# This is necessary to make Flax's generate() work
							__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id
							__SCREAMING_SNAKE_CASE									= decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= pad_token_id

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
							__SCREAMING_SNAKE_CASE									= tokenizer.convert_ids_to_tokens(model.config.pad_token_id       )

							model.save_pretrained(model_args.output_dir       )
							image_processor.save_pretrained(model_args.output_dir       )
							tokenizer.save_pretrained(model_args.output_dir       )


if __name__ == "__main__":
			main()






