




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
 from PIL import Image







class 		__UpperCAmelCase(		unittest.TestCase       ,	lowercase__ ):





     """simple docstring"""



     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple:






         """simple docstring"""
         a__      =       load_tool("""image-segmentation"""    )
         self.tool.setup()
         a__      =       load_tool("""image-segmentation"""						,				remote=A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Any:






         """simple docstring"""
         a__      =       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
         a__      =       self.tool(A						,				"""cat"""    )
         self.assertTrue(isinstance(A						,				Image.Image    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
         a__      =       self.remote_tool(A						,				"""cat"""    )
         self.assertTrue(isinstance(A						,				Image.Image    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			str:






         """simple docstring"""
         a__      =       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
         a__      =       self.tool(image=A						,				label="""cat"""    )
         self.assertTrue(isinstance(A						,				Image.Image    )    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			str:






         """simple docstring"""
         a__      =       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
         a__      =       self.remote_tool(image=A						,				label="""cat"""    )
         self.assertTrue(isinstance(A						,				Image.Image    )    )












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 ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				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]						,				A						,				atol=1E-4    )    )






