





'''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 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
#
########################################################################


a__			:					List[str]       						=						16
a__			:					int       						=						32
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Accelerator			,	SCREAMING_SNAKE_CASE_ :			int = 16 )							->     int:

       """simple docstring"""


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

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

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

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

       # Instantiate dataloaders.
       UpperCAmelCase  =						DataLoader(
           tokenized_datasets['''train''']			,	shuffle=SCREAMING_SNAKE_CASE_			,	collate_fn=SCREAMING_SNAKE_CASE_			,	batch_size=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						DataLoader(
           tokenized_datasets['''validation''']			,	shuffle=SCREAMING_SNAKE_CASE_			,	collate_fn=SCREAMING_SNAKE_CASE_			,	batch_size=SCREAMING_SNAKE_CASE_ )

       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__			:					Optional[Any]       						=						mocked_dataloaders  # noqa: F811
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[str]			,	SCREAMING_SNAKE_CASE_ :			Any )							->     Any:

       """simple docstring"""


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

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

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

       set_seed(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase,					UpperCAmelCase  =						get_dataloaders(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase  =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,	return_dict=SCREAMING_SNAKE_CASE_ )

       # 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  =						model.to(accelerator.device )

       # Instantiate optimizer
       UpperCAmelCase  =						AdamW(params=model.parameters()			,	lr=SCREAMING_SNAKE_CASE_ )

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

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE_ ):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     UpperCAmelCase  =						model(**SCREAMING_SNAKE_CASE_ )
                     UpperCAmelCase  =						outputs.loss
                     UpperCAmelCase  =						loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE_ )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              UpperCAmelCase  =						0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     with torch.no_grad():
                            UpperCAmelCase  =						model(**SCREAMING_SNAKE_CASE_ )
                     UpperCAmelCase  =						outputs.logits.argmax(dim=-1 )
                     UpperCAmelCase,					UpperCAmelCase  =						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(SCREAMING_SNAKE_CASE_ ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   UpperCAmelCase  =						predictions[: len(eval_dataloader.dataset ) - samples_seen]
                                   UpperCAmelCase  =						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=SCREAMING_SNAKE_CASE_			,	references=SCREAMING_SNAKE_CASE_			,	)

              UpperCAmelCase  =						metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:"			,	SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						argparse.ArgumentParser(description='''Simple example of training script.''' )
       parser.add_argument(
           '''--mixed_precision'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	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.''' )
       UpperCAmelCase  =						parser.parse_args()
       UpperCAmelCase  =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    main()







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase    :		Dict		      =    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(os.path.join(self.data_dir       ,	self.data[index]['img']	)	).convert('RGB'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.transforms(snake_case	)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Any						=				torch.stack([row['image_end_token'] for row in batch]   )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        __a			(   )     ->     str:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]



def        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )