
'''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 			UpperCAmelCase__    (							unittest.TestCase			):



    @slow
    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: str		    = XLMRobertaModel.from_pretrained("xlm-roberta-base"  )
          _lowerCamelCase					: Dict		    = 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

          _lowerCamelCase					: Union[str, Any]		    = torch.Size((1, 1_2, 7_6_8)  )  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase					: str		    = 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					: Optional[Any]		    = model(__A  )["last_hidden_state"].detach()
          self.assertEqual(output.shape,__A  )
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1],__A,atol=1e-3  )  )



    @slow
    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Optional[Any]		    = XLMRobertaModel.from_pretrained("xlm-roberta-large"  )
          _lowerCamelCase					: int		    = 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

          _lowerCamelCase					: Optional[Any]		    = torch.Size((1, 1_2, 1_0_2_4)  )  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase					: List[str]		    = 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					: Tuple		    = model(__A  )["last_hidden_state"].detach()
          self.assertEqual(output.shape,__A  )
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1],__A,atol=1e-3  )  )






"""simple docstring"""




import unittest

from transformers import DonutProcessor


a		:					Optional[int]	      =     '''naver-clova-ix/donut-base'''


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							DonutProcessor.from_pretrained(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Optional[Any]      =							{
								    "name": "John Doe",
								    "age": "99",
								    "city": "Atlanta",
								    "state": "GA",
								    "zip": "30301",
								    "phone": "123-4567",
								    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
								}

								a :						Tuple      =							(
								    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
								    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
								    "<s_nicknames><s_nickname>Johnny</s_nickname>"
								    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
								)
								a :						List[Any]      =							self.processor.tokenajson(lowerCAmelCase__ )

								self.assertDictEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )


