
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     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						XLMRobertaModel.from_pretrained('''xlm-roberta-base''')
												lowercase						     =						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

												lowercase						     =						torch.Size((1, 1_2, 7_6_8))  # batch_size, sequence_length, embedding_vector_dim
												lowercase						     =						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():
																	lowercase						     =						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 			A__				(    self):
												lowercase						     =						XLMRobertaModel.from_pretrained('''xlm-roberta-large''')
												lowercase						     =						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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




