
'''simple docstring'''







import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


lowercase    :							Dict      =					"__DUMMY_TRANSFORMERS_USER__"
lowercase    :							Union[str, Any]      =					"Dummy User"
lowercase    :							List[Any]      =					"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"

lowercase    :							List[str]      =					"https://hub-ci.huggingface.co"
lowercase    :							Optional[Any]      =					CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
lowercase    :							str      =					CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
lowercase    :							str      =					Path("~/.huggingface/hub_ci_token").expanduser()







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Any:
	monkeypatch.setattr(
	    'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'			,				__A	)







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	int:
	monkeypatch.setattr('datasets.config.HF_ENDPOINT'			,				__A	)
	monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'			,				__A	)







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'			,				__A	)







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Tuple:
	HfFolder.save_token(__A	)
	yield
	HfFolder.delete_token()







@pytest.fixture(scope='session'	)
def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[Any]:
	return HfApi(endpoint=__A	)







@pytest.fixture(scope='session'	)
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	int:
	_snake_case   						=		HfFolder.get_token()
	HfFolder.save_token(__A	)
	yield CI_HUB_USER_TOKEN
	if previous_token is not None:
		HfFolder.save_token(__A	)







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	def _cleanup_repo(__A	):
		hf_api.delete_repo(__A			,				token=__A			,				repo_type='dataset'	)

	return _cleanup_repo







@pytest.fixture
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
	@contextmanager
	def _temporary_repo(__A	):
		try:
			yield repo_id
		finally:
			cleanup_repo(__A	)

	return _temporary_repo







@pytest.fixture(scope='session'	)
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	_snake_case   						=		F'repo_txt_data-{int(time.time() * 10e3	)}'
	_snake_case   						=		F'{CI_HUB_USER}/{repo_name}'
	hf_api.create_repo(__A			,				token=__A			,				repo_type='dataset'			,				private=__A	)
	hf_api.upload_file(
	    token=__A			,				path_or_fileobj=str(__A	)			,				path_in_repo='data/text_data.txt'			,				repo_id=__A			,				repo_type='dataset'			,				)
	yield repo_id
	try:
		hf_api.delete_repo(__A			,				token=__A			,				repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass







@pytest.fixture()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Union[str, Any]:
	return hf_private_dataset_repo_txt_data_







@pytest.fixture(scope='session'	)
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	str:
	_snake_case   						=		F'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
	_snake_case   						=		F'{CI_HUB_USER}/{repo_name}'
	hf_api.create_repo(__A			,				token=__A			,				repo_type='dataset'			,				private=__A	)
	hf_api.upload_file(
	    token=__A			,				path_or_fileobj=str(__A	)			,				path_in_repo='data.zip'			,				repo_id=__A			,				repo_type='dataset'			,				)
	yield repo_id
	try:
		hf_api.delete_repo(__A			,				token=__A			,				repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass







@pytest.fixture()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Tuple:
	return hf_private_dataset_repo_zipped_txt_data_







@pytest.fixture(scope='session'	)
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	str:
	_snake_case   						=		F'repo_zipped_img_data-{int(time.time() * 10e3	)}'
	_snake_case   						=		F'{CI_HUB_USER}/{repo_name}'
	hf_api.create_repo(__A			,				token=__A			,				repo_type='dataset'			,				private=__A	)
	hf_api.upload_file(
	    token=__A			,				path_or_fileobj=str(__A	)			,				path_in_repo='data.zip'			,				repo_id=__A			,				repo_type='dataset'			,				)
	yield repo_id
	try:
		hf_api.delete_repo(__A			,				token=__A			,				repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass







@pytest.fixture()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Optional[int]:
	return hf_private_dataset_repo_zipped_img_data_



'''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 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		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  )  )

