






"""simple docstring"""

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     A_				(   unittest.TestCase       ,				snake_case_	):







				def      _snake_case       (				self				:    List[str]						)					->       Union[str, Any]:
										__magic_name__      =  load_tool("image-captioning"						)
										self.tool.setup()
										__magic_name__      =  load_tool("image-captioning"    ,     remote=__lowerCamelCase						)







				def      _snake_case       (				self				:    List[str]						)					->       Union[str, Any]:
										__magic_name__      =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"						)
										__magic_name__      =  self.tool(__lowerCamelCase						)
										self.assertEqual(__lowerCamelCase    ,     "two cats sleeping on a couch"						)







				def      _snake_case       (				self				:    str						)					->       List[str]:
										__magic_name__      =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"						)
										__magic_name__      =  self.remote_tool(__lowerCamelCase						)
										self.assertEqual(__lowerCamelCase    ,     "two cats sleeping on a couch"						)







				def      _snake_case       (				self				:    Optional[Any]						)					->       Dict:
										__magic_name__      =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"						)
										__magic_name__      =  self.tool(image=__lowerCamelCase						)
										self.assertEqual(__lowerCamelCase    ,     "two cats sleeping on a couch"						)







				def      _snake_case       (				self				:    Tuple						)					->       List[Any]:
										__magic_name__      =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"						)
										__magic_name__      =  self.remote_tool(image=__lowerCamelCase						)
										self.assertEqual(__lowerCamelCase    ,     "two cats sleeping on a couch"						)











"""simple docstring"""

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      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     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						)						)



