

"""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    lowerCamelCase						(unittest.TestCase			):

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      XLMRobertaModel.from_pretrained("""xlm-roberta-base"""       )
						SCREAMING_SNAKE_CASE__								=      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

						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_2, 7_6_8)       )  # batch_size, sequence_length, embedding_vector_dim
						SCREAMING_SNAKE_CASE__								=      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():
									SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      XLMRobertaModel.from_pretrained("""xlm-roberta-large"""       )
						SCREAMING_SNAKE_CASE__								=      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

						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_2, 1_0_2_4)       )  # batch_size, sequence_length, embedding_vector_dim
						SCREAMING_SNAKE_CASE__								=      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():
									SCREAMING_SNAKE_CASE__								=      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"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


