
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							import torch

							from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class      A__       (						unittest.TestCase    ):





							@slow
							def       a__			(			self  :       Tuple    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= CamembertModel.from_pretrained('camembert-base'    )
											model.to(_UpperCAmelCase    )

											__lowercase								= torch.tensor(
											    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]					,			device=_UpperCAmelCase					,			dtype=torch.long					,			)  # J'aime le camembert !
											with torch.no_grad():
															__lowercase								= model(_UpperCAmelCase    )['last_hidden_state']
											__lowercase								= torch.Size((1, 10, 7_68)    )
											self.assertEqual(output.shape					,			_UpperCAmelCase    )
											# compare the actual values for a slice.
											__lowercase								= torch.tensor(
											    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]					,			device=_UpperCAmelCase					,			dtype=torch.float					,			)
											# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
											# camembert.eval()
											# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

											self.assertTrue(torch.allclose(output[:, :3, :3]					,			_UpperCAmelCase					,			atol=1e-4    )    )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

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





