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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )

						A_		:    List[str]         =				tf.convert_to_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]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

						A_		:    Optional[Any]         =				model(lowercase )['last_hidden_state']
						A_		:    Optional[Any]         =				tf.TensorShape((1, 1_0, 7_6_8) )
						self.assertEqual(output.shape		,     lowercase )
						# compare the actual values for a slice.
						A_		:    Optional[Any]         =				tf.convert_to_tensor(
						    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     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 ) )







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






