



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


_lowercase		:				Any      		={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        snake_case__    (nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						Dict							=  self.pool(self.model(_a  )  )
									a__       :						Optional[Any]							=  torch.flatten(_a ,       start_dim=2  )
									a__       :						Any							=  out.transpose(1 ,       2  ).contiguous()
									return out  # BxNx2048




class        snake_case__    (__SCREAMING_SNAKE_CASE		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Any							=  [json.loads(_a  ) for l in open(_a  )]
									a__       :						Optional[int]							=  os.path.dirname(_a  )
									a__       :						Optional[int]							=  tokenizer
									a__       :						int							=  labels
									a__       :						Tuple							=  len(_a  )
									a__       :						Optional[int]							=  max_seq_length

									a__       :						List[Any]							=  transforms

						def __len__(       self  ) ->				Any:
									"""simple docstring"""




									return len(self.data  )

						def __getitem__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Optional[int]							=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=_a  )  )
									a__	,						a__	,						a__       :						Union[str, Any]							=  sentence[0], sentence[1:-1], sentence[-1]
									a__       :						Dict							=  sentence[: self.max_seq_length]

									a__       :						Tuple							=  torch.zeros(self.n_classes  )
									a__       :						List[str]							=  1

									a__       :						Any							=  Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
									a__       :						List[str]							=  self.transforms(_a  )

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Any							=  Counter()
									for row in self.data:
												label_freqs.update(row["""label"""]  )
									return label_freqs




def      lowerCAmelCase_					(  _lowercase    :       List[str])							->	int:







			"""simple docstring"""
			a__       :						List[Any]							=  [len(row["""sentence"""]) for row in batch]
			a__	,						a__       :						Dict							=  len(lowerCAmelCase__), max(lowerCAmelCase__)

			a__       :						int							=  torch.zeros(lowerCAmelCase__						,       lowerCAmelCase__						,       dtype=torch.long)
			a__       :						Optional[int]							=  torch.zeros(lowerCAmelCase__						,       lowerCAmelCase__						,       dtype=torch.long)

			for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__						,       lowerCAmelCase__)):
						a__       :						Union[str, Any]							=  input_row["""sentence"""]
						a__       :						Any							=  1

			a__       :						Tuple							=  torch.stack([row["""image"""] for row in batch])
			a__       :						Any							=  torch.stack([row["""label"""] for row in batch])
			a__       :						str							=  torch.stack([row["""image_start_token"""] for row in batch])
			a__       :						str							=  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      lowerCAmelCase_					(  )							->	int:







			"""simple docstring"""
			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      lowerCAmelCase_					(  )							->	str:







			"""simple docstring"""
			return transforms.Compose(
			    [
			        transforms.Resize(256),
			        transforms.CenterCrop(224),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]						,       std=[0.1222_1994, 0.1214_5835, 0.1438_0469]						,       ),
			    ])









import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase		:				Union[str, Any]      		=16
_lowercase		:				Dict      		=32

def      lowerCAmelCase_					(  _lowercase    :       Accelerator						,       _lowercase    :       int = 16						,       _lowercase    :       str = "bert-base-cased")							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  AutoTokenizer.from_pretrained(_lowercase)
			a__       :						Any							=  load_dataset("""glue"""						,       """mrpc""")

			def tokenize_function(_lowercase    :       str):
						# max_length=None => use the model max length (it's actually the default)
						a__       :						List[str]							=  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
			a__       :						Any							=  datasets.map(
			    _lowercase						,       batched=_lowercase						,       remove_columns=["""idx""", """sentence1""", """sentence2"""]						,       load_from_cache_file=_lowercase)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a__       :						Tuple							=  tokenized_datasets.rename_column("""label"""						,       """labels""")

			def collate_fn(_lowercase    :       Optional[Any]):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_lowercase						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt""")
						return tokenizer.pad(_lowercase						,       padding="""longest"""						,       return_tensors="""pt""")

			# Instantiate dataloaders.
			a__       :						int							=  DataLoader(
			    tokenized_datasets["""train"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)
			a__       :						Union[str, Any]							=  DataLoader(
			    tokenized_datasets["""validation"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)

			return train_dataloader, eval_dataloader

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







			"""simple docstring"""
			# Initialize accelerator
			a__       :						Optional[int]							=  Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__       :						Union[str, Any]							=  config["""lr"""]
			a__       :						List[str]							=  int(config["""num_epochs"""])
			a__       :						List[str]							=  int(config["""seed"""])
			a__       :						Tuple							=  int(config["""batch_size"""])
			a__       :						int							=  args.model_name_or_path

			set_seed(_lowercase)
			a__	,						a__       :						int							=  get_dataloaders(_lowercase						,       _lowercase						,       _lowercase)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__       :						Optional[Any]							=  AutoModelForSequenceClassification.from_pretrained(_lowercase						,       return_dict=_lowercase)

			# Instantiate optimizer
			a__       :						int							=  (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			a__       :						Optional[Any]							=  optimizer_cls(params=model.parameters()						,       lr=_lowercase)

			if accelerator.state.deepspeed_plugin is not None:
						a__       :						Dict							=  accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						a__       :						List[str]							=  1
			a__       :						List[Any]							=  (len(_lowercase) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						a__       :						Dict							=  get_linear_schedule_with_warmup(
						    optimizer=_lowercase						,       num_warmup_steps=0						,       num_training_steps=_lowercase						,       )
			else:
						a__       :						Dict							=  DummyScheduler(_lowercase						,       total_num_steps=_lowercase						,       warmup_num_steps=0)

			# 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.
			a__	,						a__	,						a__	,						a__	,						a__       :						List[Any]							=  accelerator.prepare(
			    _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase)

			# We need to keep track of how many total steps we have iterated over
			a__       :						Optional[int]							=  0
			# We also need to keep track of the stating epoch so files are named properly
			a__       :						Optional[int]							=  0

			# Now we train the model
			a__       :						Tuple							=  evaluate.load("""glue"""						,       """mrpc""")
			a__       :						List[Any]							=  0
			a__       :						Tuple							=  {}
			for epoch in range(_lowercase						,       _lowercase):
						model.train()
						for step, batch in enumerate(_lowercase):
									a__       :						Union[str, Any]							=  model(**_lowercase)
									a__       :						Tuple							=  outputs.loss
									a__       :						Any							=  loss / gradient_accumulation_steps
									accelerator.backward(_lowercase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						a__       :						int							=  0
						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():
												a__       :						str							=  model(**_lowercase)
									a__       :						Union[str, Any]							=  outputs.logits.argmax(dim=-1)
									# It is slightly faster to call this once, than multiple times
									a__	,						a__       :						Optional[int]							=  accelerator.gather(
									    (predictions, batch["""labels"""]))  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(_lowercase) - 1:
															a__       :						Union[str, Any]							=  predictions[: len(eval_dataloader.dataset) - samples_seen]
															a__       :						Any							=  references[: len(eval_dataloader.dataset) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=_lowercase						,       references=_lowercase						,       )

						a__       :						Optional[Any]							=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''						,       _lowercase)
						a__       :						Any							=  eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									a__       :						List[str]							=  eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir						,       """all_results.json""")						,       """w""") as f:
									json.dump(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  )							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
			parser.add_argument(
			    """--model_name_or_path"""						,       type=_lowercase						,       default="""bert-base-cased"""						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       required=_lowercase						,       )
			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(
			    """--performance_lower_bound"""						,       type=_lowercase						,       default=_lowercase						,       help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""						,       )
			parser.add_argument(
			    """--num_epochs"""						,       type=_lowercase						,       default=3						,       help="""Number of train epochs."""						,       )
			a__       :						Any							=  parser.parse_args()
			a__       :						Dict							=  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(_lowercase						,       _lowercase)


if __name__ == "__main__":
		main()




