





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 SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@slow
						def UpperCAmelCase_			( self : int		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		XLMRobertaModel.from_pretrained('xlm-roberta-base'		)
							snake_case_						:     Any     =		torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]		)
							# The dog is cute and lives in the garden house

							snake_case_						:     List[str]     =		torch.Size((1, 12, 768)		)  # batch_size, sequence_length, embedding_vector_dim
							snake_case_						:     str     =		torch.tensor(
							    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)
							#  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():
								snake_case_						:     Union[str, Any]     =		model(_lowerCamelCase		)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape						,				_lowerCamelCase		)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,				_lowerCamelCase						,				atol=1E-3		)		)






						@slow
						def UpperCAmelCase_			( self : str		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		XLMRobertaModel.from_pretrained('xlm-roberta-large'		)
							snake_case_						:     Union[str, Any]     =		torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]		)
							# The dog is cute and lives in the garden house

							snake_case_						:     Tuple     =		torch.Size((1, 12, 1024)		)  # batch_size, sequence_length, embedding_vector_dim
							snake_case_						:     Union[str, Any]     =		torch.tensor(
							    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]		)
							#  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():
								snake_case_						:     Tuple     =		model(_lowerCamelCase		)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape						,				_lowerCamelCase		)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,				_lowerCamelCase						,				atol=1E-3		)		)












from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

