
"""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 __lowerCAmelCase		(				unittest.TestCase      ):





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFCamembertModel.from_pretrained('jplu/tf-camembert-base'				)

      __UpperCamelCase      =	tf.convert_to_tensor(
          [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]     ,					dtype=tf.intaa     ,					)  # J'aime le camembert !"

      __UpperCamelCase      =	model(__UpperCAmelCase				)['last_hidden_state']
      __UpperCamelCase      =	tf.TensorShape((1, 10, 768)				)
      self.assertEqual(output.shape     ,					__UpperCAmelCase				)
      # compare the actual values for a slice.
      __UpperCamelCase      =	tf.convert_to_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]]]     ,					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				)				)





"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def 							A						(    )				->     Any:
 __UpperCamelCase      =	{
     'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
     'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
     'content': ['a ' * 2_0, 'a ' * 3_0, 'b ' * 7],
 }
 __UpperCamelCase      =	Dataset.from_dict(snake_case		)
 return dataset





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase      =	make_duplicate_clusters(__UpperCAmelCase     ,					0.8_5				)
      self.assertEqual(len(duplicate_clusters[0]				)     ,					2				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase  ,     __UpperCamelCase      =	deduplicate_dataset(__UpperCAmelCase				)
      self.assertEqual(len(__UpperCAmelCase				)     ,					2				)
      print(__UpperCAmelCase				)
      self.assertEqual(duplicate_clusters[0][0]['copies']     ,					2				)
      self.assertEqual(duplicate_clusters[0][0]['is_extreme']     ,					__UpperCAmelCase				)



