



'''simple docstring'''




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


a   :			List[str]					  =    {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 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self		,		A    )			->			str:
								super().__init__()
								UpperCAmelCase     :		str							     =		torchvision.models.resnetaaa(pretrained=A    )
								UpperCAmelCase     :		List[str]							     =		list(model.children()    )[:-2]
								UpperCAmelCase     :		Any							     =		nn.Sequential(*A    )
								UpperCAmelCase     :		int							     =		nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )






	def  _lowercase(  self		,		A    )			->			List[str]:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase     :		str							     =		self.pool(self.model(A    )    )
								UpperCAmelCase     :		Dict							     =		torch.flatten(A		,		start_dim=2    )
								UpperCAmelCase     :		int							     =		out.transpose(1		,		2    ).contiguous()
								return out  # BxNx2048




class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		List[str]							     =		[json.loads(A    ) for l in open(A    )]
								UpperCAmelCase     :		List[Any]							     =		os.path.dirname(A    )
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer
								UpperCAmelCase     :		Tuple							     =		labels
								UpperCAmelCase     :		List[Any]							     =		len(A    )
								UpperCAmelCase     :		Dict							     =		max_seq_length

								UpperCAmelCase     :		List[Any]							     =		transforms





	def __len__(  self    )			->			Optional[int]:
								return len(self.data    )





	def __getitem__(  self		,		A    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]		,		add_special_tokens=A    )    )
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase     :		List[Any]							     =		sentence[: self.max_seq_length]

								UpperCAmelCase     :		Optional[int]							     =		torch.zeros(self.n_classes    )
								UpperCAmelCase     :		str							     =		1

								UpperCAmelCase     :		List[Any]							     =		Image.open(os.path.join(self.data_dir		,		self.data[index]["""img"""]    )    ).convert("""RGB"""    )
								UpperCAmelCase     :		Optional[int]							     =		self.transforms(A    )

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






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		Counter()
								for row in self.data:
															label_freqs.update(row["""label"""]    )
								return label_freqs




def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							UpperCAmelCase     :		Dict							     =		[len(row["""sentence"""]		) for row in batch]
							UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		len(_lowercase		), max(_lowercase		)

							UpperCAmelCase     :		Dict							     =		torch.zeros(_lowercase	,				_lowercase	,				dtype=torch.long		)
							UpperCAmelCase     :		str							     =		torch.zeros(_lowercase	,				_lowercase	,				dtype=torch.long		)

							for i_batch, (input_row, length) in enumerate(zip(_lowercase	,				_lowercase		)		):
														UpperCAmelCase     :		List[Any]							     =		input_row["""sentence"""]
														UpperCAmelCase     :		List[str]							     =		1

							UpperCAmelCase     :		Union[str, Any]							     =		torch.stack([row["""image"""] for row in batch]		)
							UpperCAmelCase     :		Any							     =		torch.stack([row["""label"""] for row in batch]		)
							UpperCAmelCase     :		List[Any]							     =		torch.stack([row["""image_start_token"""] for row in batch]		)
							UpperCAmelCase     :		Optional[int]							     =		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  (					)      ->			Tuple:
							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  (					)      ->			List[str]:
							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6		),
							        transforms.CenterCrop(2_2_4		),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]	,				std=[0.1222_1994, 0.1214_5835, 0.1438_0469]	,				),
							    ]		)






'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

