
'''simple docstring'''





import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch





class 			_lowerCAmelCase      (			unittest.TestCase ):
				_lowercase    	=JukeboxTokenizer
				_lowercase    	={
				    '''artist''': '''Zac Brown Band''',
				    '''genres''': '''Country''',
				    '''lyrics''': '''I met a traveller from an antique land,\n        Who said "Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ''',
				}




				@require_torch
				def   __a		(      self  )       ->   str:
							import torch

							lowerCAmelCase_      			=			JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"  )
							lowerCAmelCase_      			=			tokenizer(**self.metas  )["input_ids"]
							# fmt: off
							lowerCAmelCase_      			=			[
							    torch.tensor([[
							        0, 0, 0, 7_169, 507, 9, 76, 39, 31, 46, 76, 27,
							        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
							        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
							        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
							        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
							        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
							        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
							        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
							        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
							        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
							        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
							        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
							        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
							        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
							        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
							        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
							        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
							        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
							        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
							        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
							        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
							        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
							        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
							        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
							        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
							        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
							        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
							        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
							        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
							        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
							        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
							        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
							        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
							        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
							        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
							        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
							        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
							        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
							        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
							        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
							        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
							        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
							        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
							        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
							        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
							        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
							        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
							        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
							        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
							        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
							        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
							        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
							        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
							        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
							        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
							        76, 76]]  ),
							    torch.tensor([[0, 0, 0, 1_069, 11]]  ),
							    torch.tensor([[0, 0, 0, 1_069, 11]]  ),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]       ,       EXPECTED_OUTPUT[0]  )  )
							self.assertTrue(torch.allclose(tokens[1]       ,       EXPECTED_OUTPUT[1]  )  )
							self.assertTrue(torch.allclose(tokens[2]       ,       EXPECTED_OUTPUT[2]  )  )




				@require_torch
				def   __a		(      self  )       ->   Dict:
							import torch

							lowerCAmelCase_      			=			JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"  )
							lowerCAmelCase_      			=			tokenizer(**self.metas  )["input_ids"]
							# fmt: off
							lowerCAmelCase_      			=			[
							    torch.tensor([[
							        0, 0, 0, 1_069, 11, -1, -1, -1, -1, 9, 77, 39,
							        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
							        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
							        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
							        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
							        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
							        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
							        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
							        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
							        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
							        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
							        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
							        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
							        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
							        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
							        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
							        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
							        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
							        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
							        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
							        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
							        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
							        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
							        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
							        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
							        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
							        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
							        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
							        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
							        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
							        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
							        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
							        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
							        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
							        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
							        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
							        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
							        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
							        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
							        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
							        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
							        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
							        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
							        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
							        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
							        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
							        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
							        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
							        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
							        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
							        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
							        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
							        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
							        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
							        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
							        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
							        77, 77, 77, 77, 77, 77]]  ),
							    torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]  ),
							    torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]  ),
							]
							# fmt: on
							self.assertTrue(torch.allclose(tokens[0]       ,       EXPECTED_OUTPUT[0]  )  )
							self.assertTrue(torch.allclose(tokens[1]       ,       EXPECTED_OUTPUT[1]  )  )
							self.assertTrue(torch.allclose(tokens[2]       ,       EXPECTED_OUTPUT[2]  )  )





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     =  {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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							super().__init__()
							lowerCAmelCase_      			=			torchvision.models.resnetaaa(pretrained=_UpperCamelCase  )
							lowerCAmelCase_      			=			list(model.children()  )[:-2]
							lowerCAmelCase_      			=			nn.Sequential(*_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict:
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							lowerCAmelCase_      			=			self.pool(self.model(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			torch.flatten(_UpperCamelCase       ,       start_dim=2  )
							lowerCAmelCase_      			=			out.transpose(1       ,       2  ).contiguous()
							return out  # BxNx2048





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			[json.loads(_UpperCamelCase  ) for l in open(_UpperCamelCase  )]
							lowerCAmelCase_      			=			os.path.dirname(_UpperCamelCase  )
							lowerCAmelCase_      			=			tokenizer
							lowerCAmelCase_      			=			labels
							lowerCAmelCase_      			=			len(_UpperCamelCase  )
							lowerCAmelCase_      			=			max_seq_length

							lowerCAmelCase_      			=			transforms




				def __len__(      self  )       ->   Any:
							return len(self.data  )




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

							lowerCAmelCase_      			=			torch.zeros(self.n_classes  )
							lowerCAmelCase_      			=			1

							lowerCAmelCase_      			=			Image.open(os.path.join(self.data_dir       ,       self.data[index]["img"]  )  ).convert("RGB"  )
							lowerCAmelCase_      			=			self.transforms(_UpperCamelCase  )

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




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			Counter()
							for row in self.data:
										label_freqs.update(row["label"]  )
							return label_freqs






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[len(row["sentence"]       ) for row in batch]
			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), max(__lowerCAmelCase       )

			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )
			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )

			for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       ):
						lowerCAmelCase_      			=			input_row["sentence"]
						lowerCAmelCase_      			=			1

			lowerCAmelCase_      			=			torch.stack([row["image"] for row in batch]       )
			lowerCAmelCase_      			=			torch.stack([row["label"] for row in batch]       )
			lowerCAmelCase_      			=			torch.stack([row["image_start_token"] for row in batch]       )
			lowerCAmelCase_      			=			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__       (       ):




			"""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__       (       ):




			"""simple docstring"""


			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]					,							),
			    ]       )



