





"""simple docstring"""



import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_					=     get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						lowercase_				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	XLNetTokenizer
 _lowerCamelCase								=	XLNetTokenizerFast
 _lowerCamelCase								=	True
 _lowerCamelCase								=	True

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A       :		Optional[Any]							=	XLNetTokenizer(lowerCamelCase_   ,		keep_accents=lowerCamelCase_    )
     tokenizer.sanitize_special_tokens()
     tokenizer.save_pretrained(self.tmpdirname    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	"""<s>"""
     __A       :		List[str]							=	1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_    )   ,		lowerCamelCase_    )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_    )   ,		lowerCamelCase_    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]   ,		'''<unk>'''    )
     self.assertEqual(vocab_keys[1]   ,		'''<s>'''    )
     self.assertEqual(vocab_keys[-1]   ,		'''<eod>'''    )
     self.assertEqual(len(lowerCamelCase_    )   ,		1006    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size   ,		1000    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	XLNetTokenizer(lowerCamelCase_   ,		keep_accents=lowerCamelCase_    )

     __A       :		Any							=	tokenizer.tokenize('''This is a test'''    )
     self.assertListEqual(lowerCamelCase_   ,		['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    )   ,		[285, 46, 10, 170, 382]    )

     __A       :		List[str]							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         lowerCamelCase_   ,		[
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ]   ,		)
     __A       :		Optional[int]							=	tokenizer.convert_tokens_to_ids(lowerCamelCase_    )
     self.assertListEqual(lowerCamelCase_   ,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]    )

     __A       :		int							=	tokenizer.convert_ids_to_tokens(lowerCamelCase_    )
     self.assertListEqual(
         lowerCamelCase_   ,		[
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''<unk>''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''<unk>''',
             '''.''',
         ]   ,		)

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	XLNetTokenizer(lowerCamelCase_   ,		do_lower_case=lowerCamelCase_    )
     __A       :		List[Any]							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         lowerCamelCase_   ,		[
             SPIECE_UNDERLINE + '''''',
             '''i''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''se''',
             '''.''',
         ]   ,		)
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''    )   ,		['''▁he''', '''ll''', '''o''']    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	XLNetTokenizer(lowerCamelCase_   ,		do_lower_case=lowerCamelCase_    )
     __A       :		List[str]							=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
     self.assertListEqual(
         lowerCamelCase_   ,		[
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''se''',
             '''.''',
         ]   ,		)

 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	XLNetTokenizer.from_pretrained('''xlnet-base-cased'''    )

     __A       :		int							=	tokenizer.encode('''sequence builders'''   ,		add_special_tokens=lowerCamelCase_    )
     __A       :		List[str]							=	tokenizer.encode('''multi-sequence build'''   ,		add_special_tokens=lowerCamelCase_    )

     __A       :		Optional[int]							=	tokenizer.build_inputs_with_special_tokens(lowerCamelCase_    )
     __A       :		List[Any]							=	tokenizer.build_inputs_with_special_tokens(lowerCamelCase_   ,		lowerCamelCase_    )

     assert encoded_sentence == text + [4, 3]
     assert encoded_pair == text + [4] + text_a + [4, 3]







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	{"""input_ids""": [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCamelCase_   ,		model_name='''xlnet-base-cased'''   ,		revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''   ,		)











'''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


__UpperCAmelCase            =       {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				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



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







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

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

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			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		(  ):


		"""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 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        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]			,			),
		    ]	)



