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					):
		"""simple docstring"""







		@slow
		def    __A     (							self :       Dict   )  ->	Any:
								SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
								SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
								# The dog is cute and lives in the garden house

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







		@slow
		def    __A     (							self :       Tuple   )  ->	List[str]:
								SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
								SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
								# The dog is cute and lives in the garden house

								SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
								SCREAMING_SNAKE_CASE_          =  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():
														SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
								self.assertEqual(output.shape  ,     __magic_name__   )
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None				):
      if version.parse(hfh.__version__				).release < version.parse("0.11.0"				).release:
            # old versions of hfh don't url-encode the file path
            SCREAMING_SNAKE_CASE_          =  quote(__UpperCamelCase				)
      return hfh.hf_hub_url(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"     ,      revision=__UpperCamelCase				)


