

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 ):







    """simple docstring"""





    @slow
    def 						snake_case__     (      self :       Any		)->    List[str]:





        '''simple docstring'''







        A__          =   XLMRobertaModel.from_pretrained('xlm-roberta-base'		)
        A__          =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
        # The dog is cute and lives in the garden house

        A__          =   torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
        A__          =   torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]		)
        #  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(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 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        A__          =   XLMRobertaModel.from_pretrained('xlm-roberta-large'		)
        A__          =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
        # The dog is cute and lives in the garden house

        A__          =   torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
        A__          =   torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]		)
        #  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(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		)		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

