

"""simple docstring"""


from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url




@pytest.mark.parametrize("""repo_id"""  ,					["""canonical_dataset_name""", """org-name/dataset-name"""]							)
@pytest.mark.parametrize("""path"""  ,					["""filename.csv""", """filename with blanks.csv"""]							)
@pytest.mark.parametrize("""revision"""  ,					[None, """v2"""]							)
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[Any]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	hf_hub_url(repo_id=__SCREAMING_SNAKE_CASE  ,					path=__SCREAMING_SNAKE_CASE  ,					revision=__SCREAMING_SNAKE_CASE							)
      assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(__SCREAMING_SNAKE_CASE							)}'''







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.convert_to_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]]]	,    dtype=tf.floataa	,    )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




