






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 			__a  (    unittest.TestCase       ):







 @slow
 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  XLMRobertaModel.from_pretrained("xlm-roberta-base"						)
      UpperCamelCase__			:   List[Any]       				=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]						)
      # The dog is cute and lives in the garden house

      UpperCamelCase__			:   Optional[Any]       				=  torch.Size((1, 12, 7_68)						)  # batch_size, sequence_length, embedding_vector_dim
      UpperCamelCase__			:   Any       				=  torch.tensor(
          [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
      #  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():
           UpperCamelCase__			:   Union[str, Any]       				=  model(__lowercase						)["last_hidden_state"].detach()
      self.assertEqual(output.shape							,   __lowercase						)
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]							,   __lowercase							,   atol=1e-3						)						)







 @slow
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  XLMRobertaModel.from_pretrained("xlm-roberta-large"						)
      UpperCamelCase__			:   Optional[Any]       				=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]						)
      # The dog is cute and lives in the garden house

      UpperCamelCase__			:   Union[str, Any]       				=  torch.Size((1, 12, 10_24)						)  # batch_size, sequence_length, embedding_vector_dim
      UpperCamelCase__			:   Dict       				=  torch.tensor(
          [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
      #  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():
           UpperCamelCase__			:   Any       				=  model(__lowercase						)["last_hidden_state"].detach()
      self.assertEqual(output.shape							,   __lowercase						)
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]							,   __lowercase							,   atol=1e-3						)						)





import functools


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							) or not all(isinstance(_A     ,	_A							) for day in days							):
		raise ValueError("The parameter days should be a list of integers"							)

	if len(_A							) != 3 or not all(isinstance(_A     ,	_A							) for cost in costs							):
		raise ValueError("The parameter costs should be a list of three integers"							)

	if len(_A							) == 0:
		return 0

	if min(_A							) <= 0:
		raise ValueError("All days elements should be greater than 0"							)

	if max(_A							) >= 366:
		raise ValueError("All days elements should be less than 366"							)

	snake_case_		    =     set(_A							)

	@functools.cache
	def dynamic_programming(_A							) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1							)

		return min(
		    costs[0] + dynamic_programming(index + 1							)     ,	costs[1] + dynamic_programming(index + 7							)     ,	costs[2] + dynamic_programming(index + 30							)     ,	)

	return dynamic_programming(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
