
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase      :							List[Any]          =					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							int          =					["DetrFeatureExtractor"]
      UpperCamelCase      :							Any          =					["DetrImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      :							List[Any]          =					[
          "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DetrForObjectDetection",
          "DetrForSegmentation",
          "DetrModel",
          "DetrPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_detr import DetrFeatureExtractor
            from .image_processing_detr import DetrImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_detr import (
                DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetrForObjectDetection,
                DetrForSegmentation,
                DetrModel,
                DetrPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase      :							Union[str, Any]          =					_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, 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		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						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			)			)




