




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

class     __a(	unittest.TestCase  ):






    """simple docstring"""





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





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

         UpperCAmelCase_     :				int  =     JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''    )
         UpperCAmelCase_     :				List[str]  =     tokenizer(**self.metas    )['''input_ids''']
         # fmt: off
         UpperCAmelCase_     :				List[Any]  =     [
             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    )					->						List[str]:
         import torch

         UpperCAmelCase_     :				List[Any]  =     JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''    )
         UpperCAmelCase_     :				int  =     tokenizer(**self.metas    )['''input_ids''']
         # fmt: off
         UpperCAmelCase_     :				Dict  =     [
             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


_lowercase							:   Union[str, 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    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







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

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






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

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

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

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

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

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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

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

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					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					( )			-> Union[str, Any]:





	"""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					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



