




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@slow
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							XLMRobertaModel.from_pretrained("""xlm-roberta-base"""				)
								a										=							torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]				)
								# The dog is cute and lives in the garden house

								a										=							torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
								a										=							torch.tensor(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
								#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
								#  xlmr.eval()
								#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
								with torch.no_grad():
									a										=							model(__magic_name__				)["""last_hidden_state"""].detach()
								self.assertEqual(output.shape		,      __magic_name__				)
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]		,      __magic_name__		,      atol=1E-3				)				)





							@slow
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							XLMRobertaModel.from_pretrained("""xlm-roberta-large"""				)
								a										=							torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]				)
								# The dog is cute and lives in the garden house

								a										=							torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
								a										=							torch.tensor(
								    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
								#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
								#  xlmr.eval()
								#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
								with torch.no_grad():
									a										=							model(__magic_name__				)["""last_hidden_state"""].detach()
								self.assertEqual(output.shape		,      __magic_name__				)
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]		,      __magic_name__		,      atol=1E-3				)				)







from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = None    )      ->       list[list[str]]:
	a										=							word_bank or []
	# create a table
	a										=							len(__lowerCamelCase    ) + 1

	a										=							[]
	for _ in range(__lowerCamelCase    ):
		table.append([]    )
	# seed value
	a										=							[[]]  # because empty string has empty combination

	# iterate through the indices
	for i in range(__lowerCamelCase    ):
		# condition
		if table[i] != []:
			for word in word_bank:
				# slice condition
				if target[i : i + len(__lowerCamelCase    )] == word:
					a										=							[
					    [word, *way] for way in table[i]
					]
					# adds the word to every combination the current position holds
					# now,push that combination to the table[i+len(word)]
					table[i + len(__lowerCamelCase    )] += new_combinations

    # combinations are in reverse order so reverse for better output
	for combination in table[len(__lowerCamelCase    )]:
		combination.reverse()

	return table[len(__lowerCamelCase    )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)

