

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   __UpperCamelCase      (   unittest.TestCase  ):




	@slow
	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CamembertModel.from_pretrained("camembert-base"							)
		model.to(lowerCAmelCase							)

		UpperCAmelCase_    			=     torch.tensor(
		    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]						,		device=lowerCAmelCase						,		dtype=torch.long						,		)  # J'aime le camembert !
		with torch.no_grad():
			UpperCAmelCase_    			=     model(lowerCAmelCase							)["last_hidden_state"]
		UpperCAmelCase_    			=     torch.Size((1, 10, 768)							)
		self.assertEqual(output.shape						,		lowerCAmelCase							)
		# compare the actual values for a slice.
		UpperCAmelCase_    			=     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=lowerCAmelCase						,		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]						,		lowerCAmelCase						,		atol=1e-4							)							)


from typing import TYPE_CHECKING

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


_a:      Tuple								=     {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      List[str]								=     ["""SegformerFeatureExtractor"""]
				_a:      Union[str, Any]								=     ["""SegformerImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SegformerDecodeHead""",
				    """SegformerForImageClassification""",
				    """SegformerForSemanticSegmentation""",
				    """SegformerLayer""",
				    """SegformerModel""",
				    """SegformerPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Optional[Any]								=     [
				    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFSegformerDecodeHead""",
				    """TFSegformerForImageClassification""",
				    """TFSegformerForSemanticSegmentation""",
				    """TFSegformerModel""",
				    """TFSegformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				_a:      Optional[int]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)