"""simple docstring"""




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     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = XLMRobertaModel.from_pretrained("""xlm-roberta-base"""  )
							lowerCAmelCase__   = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]  )
							# The dog is cute and lives in the garden house

							lowerCAmelCase__   = torch.Size((1, 12, 768)  )  # batch_size, sequence_length, embedding_vector_dim
							lowerCAmelCase__   = 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():
									lowerCAmelCase__   = model(snake_case__  )["""last_hidden_state"""].detach()
							self.assertEqual(output.shape	,    snake_case__  )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]	,    snake_case__	,    atol=1E-3  )  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = XLMRobertaModel.from_pretrained("""xlm-roberta-large"""  )
							lowerCAmelCase__   = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]  )
							# The dog is cute and lives in the garden house

							lowerCAmelCase__   = torch.Size((1, 12, 1024)  )  # batch_size, sequence_length, embedding_vector_dim
							lowerCAmelCase__   = 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():
									lowerCAmelCase__   = model(snake_case__  )["""last_hidden_state"""].detach()
							self.assertEqual(output.shape	,    snake_case__  )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]	,    snake_case__	,    atol=1E-3  )  )





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




