



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase_   						=   {
    "configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverOnnxConfig"],
    "tokenization_perceiver": ["PerceiverTokenizer"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ["PerceiverFeatureExtractor"]
							lowerCAmelCase_   						=   ["PerceiverImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "PerceiverForImageClassificationConvProcessing",
							    "PerceiverForImageClassificationFourier",
							    "PerceiverForImageClassificationLearned",
							    "PerceiverForMaskedLM",
							    "PerceiverForMultimodalAutoencoding",
							    "PerceiverForOpticalFlow",
							    "PerceiverForSequenceClassification",
							    "PerceiverLayer",
							    "PerceiverModel",
							    "PerceiverPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
							from .tokenization_perceiver import PerceiverTokenizer

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_perceiver import PerceiverFeatureExtractor
														from .image_processing_perceiver import PerceiverImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_perceiver import (
														    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PerceiverForImageClassificationConvProcessing,
														    PerceiverForImageClassificationFourier,
														    PerceiverForImageClassificationLearned,
														    PerceiverForMaskedLM,
														    PerceiverForMultimodalAutoencoding,
														    PerceiverForOpticalFlow,
														    PerceiverForSequenceClassification,
														    PerceiverLayer,
														    PerceiverModel,
														    PerceiverPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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			):
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
				UpperCAmelCase   		=				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

				UpperCAmelCase   		=				torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]		)
				#  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():
							UpperCAmelCase   		=				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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
				UpperCAmelCase   		=				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

				UpperCAmelCase   		=				torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]		)
				#  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():
							UpperCAmelCase   		=				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		)		)



