

import time
from contextlib import contextmanager
from pathlib import Path

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


lowerCAmelCase__								=		"""__DUMMY_TRANSFORMERS_USER__"""
lowerCAmelCase__								=		"""Dummy User"""
lowerCAmelCase__								=		"""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"""

lowerCAmelCase__								=		"""https://hub-ci.huggingface.co"""
lowerCAmelCase__								=		CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
lowerCAmelCase__								=		CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
lowerCAmelCase__								=		Path("""~/.huggingface/hub_ci_token""").expanduser()






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				Dict:




	'''simple docstring'''




	monkeypatch.setattr(
	    'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'							,       UpperCAmelCase_	)






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				Optional[Any]:




	'''simple docstring'''




	monkeypatch.setattr('datasets.config.HF_ENDPOINT'							,       UpperCAmelCase_	)
	monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'							,       UpperCAmelCase_	)






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]	)						->				Optional[int]:




	'''simple docstring'''




	monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'							,       UpperCAmelCase_	)






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Union[str, Any]	)						->				Optional[int]:




	'''simple docstring'''




	HfFolder.save_token(UpperCAmelCase_	)
	yield
	HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 					lowerCamelCase_     (							)						->				int:




	'''simple docstring'''




	return HfApi(endpoint=UpperCAmelCase_	)






@pytest.fixture(scope='session'	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     HfApi	)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						HfFolder.get_token()
	HfFolder.save_token(UpperCAmelCase_	)
	yield CI_HUB_USER_TOKEN
	if previous_token is not None:
		HfFolder.save_token(UpperCAmelCase_	)






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Any:




	'''simple docstring'''




	def _cleanup_repo(UpperCAmelCase_							:     List[str]	):
		hf_api.delete_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'	)

	return _cleanup_repo






@pytest.fixture
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				int:




	'''simple docstring'''




	@contextmanager
	def _temporary_repo(UpperCAmelCase_							:     Union[str, Any]	):
		try:
			yield repo_id
		finally:
			cleanup_repo(UpperCAmelCase_	)

	return _temporary_repo






@pytest.fixture(scope='session'	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     HfApi							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						F'''repo_txt_data-{int(time.time() * 1_0e3	)}'''
	_UpperCamelCase   :		Union[str, Any]			=						F'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'							,       private=UpperCAmelCase_	)
	hf_api.upload_file(
	    token=UpperCAmelCase_							,       path_or_fileobj=str(UpperCAmelCase_	)							,       path_in_repo='data/text_data.txt'							,       repo_id=UpperCAmelCase_							,       repo_type='dataset'							,       )
	yield repo_id
	try:
		hf_api.delete_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass






@pytest.fixture()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     List[str]	)						->				Union[str, Any]:




	'''simple docstring'''




	return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     HfApi							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[int]	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						F'''repo_zipped_txt_data-{int(time.time() * 1_0e3	)}'''
	_UpperCamelCase   :		str			=						F'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'							,       private=UpperCAmelCase_	)
	hf_api.upload_file(
	    token=UpperCAmelCase_							,       path_or_fileobj=str(UpperCAmelCase_	)							,       path_in_repo='data.zip'							,       repo_id=UpperCAmelCase_							,       repo_type='dataset'							,       )
	yield repo_id
	try:
		hf_api.delete_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass






@pytest.fixture()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Tuple	)						->				str:




	'''simple docstring'''




	return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     HfApi							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Dict	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						F'''repo_zipped_img_data-{int(time.time() * 1_0e3	)}'''
	_UpperCamelCase   :		int			=						F'''{CI_HUB_USER}/{repo_name}'''
	hf_api.create_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'							,       private=UpperCAmelCase_	)
	hf_api.upload_file(
	    token=UpperCAmelCase_							,       path_or_fileobj=str(UpperCAmelCase_	)							,       path_in_repo='data.zip'							,       repo_id=UpperCAmelCase_							,       repo_type='dataset'							,       )
	yield repo_id
	try:
		hf_api.delete_repo(UpperCAmelCase_							,       token=UpperCAmelCase_							,       repo_type='dataset'	)
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass







@pytest.fixture()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     List[str]	)						->				List[str]:




	'''simple docstring'''




	return hf_private_dataset_repo_zipped_img_data_









import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase__								=		["""bert-base-uncased""", """bert-base-cased"""]
lowerCAmelCase__								=		"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():







							class 	lowercase    (				tf.keras.Model							):


									"""simple docstring"""




									def __init__(				self	,       __snake_case):
										super().__init__()
										_UpperCamelCase   :		List[Any]			=						tokenizer
										_UpperCamelCase   :		List[Any]			=						AutoConfig.from_pretrained(__snake_case)
										_UpperCamelCase   :		Dict			=						TFAutoModel.from_config(__snake_case)






									def 		A__					(				self	,       __snake_case):
										_UpperCamelCase   :		Any			=						self.tokenizer(__snake_case)
										_UpperCamelCase   :		Dict			=						self.bert(**__snake_case)
										return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self):
			super().setUp()

			_UpperCamelCase   :		Optional[Any]			=						[
			    BertTokenizer.from_pretrained(__snake_case) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			_UpperCamelCase   :		Optional[Any]			=						[TFBertTokenizer.from_pretrained(__snake_case) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(__snake_case	,       use_fast_bert_tokenizer=__snake_case)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers) == len(self.tf_tokenizers)

			_UpperCamelCase   :		Optional[Any]			=						[
			    'This is a straightforward English test sentence.',
			    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
			    'Now we\'re going to add some Chinese: 一 二 三 一二三',
			    'And some much more rare Chinese: 齉 堃 齉堃',
			    'Je vais aussi écrire en français pour tester les accents',
			    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
			]
			_UpperCamelCase   :		Dict			=						list(zip(self.test_sentences	,       self.test_sentences[::-1]))


		def 		A__					(				self):
			for tokenizer, tf_tokenizer in zip(self.tokenizers	,       self.tf_tokenizers):
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		List[str]			=						tokenizer(__snake_case	,       return_tensors='tf'	,       padding='longest')
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in python_outputs.keys():
						self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
						self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,       tf.intaa) == tf_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf_tokenizer(self.paired_sentences)
				_UpperCamelCase   :		Optional[Any]			=						tf_tokenizer(
				    text=[sentence[0] for sentence in self.paired_sentences]	,       text_pair=[sentence[1] for sentence in self.paired_sentences]	,       )
				for key in merged_outputs.keys():
					self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,       tf.intaa) == separated_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf.function(__snake_case)
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		Optional[int]			=						tf.constant(__snake_case)
					_UpperCamelCase   :		Union[str, Any]			=						compiled_tokenizer(__snake_case)
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in eager_outputs.keys():
						self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))






		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Any			=						ModelToSave(tokenizer=__snake_case)
				_UpperCamelCase   :		Any			=						tf.convert_to_tensor(self.test_sentences)
				_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)  # Build model with some sample inputs
				with TemporaryDirectory() as tempdir:
					_UpperCamelCase   :		int			=						Path(__snake_case) / 'saved.model'
					model.save(__snake_case)
					_UpperCamelCase   :		Optional[int]			=						tf.keras.models.load_model(__snake_case)
				_UpperCamelCase   :		int			=						loaded_model(__snake_case)
				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output))	,       1e-5)




