
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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @slow
  def 							_lowercase							( self			: Dict    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       XLMRobertaModel.from_pretrained("""xlm-roberta-base"""    )
    __magic_name__				     =       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

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





  @slow
  def 							_lowercase							( self			: Tuple    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       XLMRobertaModel.from_pretrained("""xlm-roberta-large"""    )
    __magic_name__				     =       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

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








"""simple docstring"""
def 					A__				(			UpperCamelCase       ):
							A		=  generate_pascal_triangle(UpperCamelCase       )
							for row_idx in range(UpperCamelCase       ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1       ):
																					print(end=" "       )
														# Print row values
														for col_idx in range(row_idx + 1       ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]					,  end=" "       )
																					else:
																												print(triangle[row_idx][col_idx]					,  end=""       )
														print()






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  []
							for current_row_idx in range(UpperCamelCase       ):
														A		=  populate_current_row(UpperCamelCase					,  UpperCamelCase       )
														triangle.append(UpperCamelCase       )
							return triangle






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							A,	A		=  1, 1
							for current_col_idx in range(1					,  UpperCamelCase       ):
														calculate_current_element(
														    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
							return current_row






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  ):
							A		=  triangle[current_row_idx - 1][current_col_idx - 1]
							A		=  triangle[current_row_idx - 1][current_col_idx]
							A		=  above_to_left_elt + above_to_right_elt






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  [[1]]

							for row_index in range(1					,  UpperCamelCase       ):
														A		=  [0] + result[-1] + [0]
														A		=  row_index + 1
														# Calculate the number of distinct elements in a row
														A		=  sum(divmod(UpperCamelCase					,  2       )       )
														A		=  [
														    temp_row[i - 1] + temp_row[i] for i in range(1					,  distinct_elements + 1       )
														]
														A		=  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														A		=  row_first_half + row_second_half
														result.append(UpperCamelCase       )

							return result






def 					A__				(			):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(UpperCamelCase					,  UpperCamelCase       ) -> None:
														A		=  F"{func.__name__}({value})"
														A		=  timeit(F"__main__.{call}"					,  setup="import __main__"       )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F"{call:38} -- {timing:.4f} seconds"       )

							for value in range(15       ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(UpperCamelCase					,  UpperCamelCase       )
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()


