





'''simple docstring'''






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


A   : int             =			[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]





def       lowercase_      (		lowercase__=True						)   ->Dict:
						if with_config:
												return [
												    {
												        "testcase_name": d["dataset"] + "/" + d["config_name"],
												        "dataset": d["dataset"],
												        "config_name": d["config_name"],
												    }
												    for d in DATASETS_ON_HF_GCP
												]
						else:
												return [
												    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
												]







@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase  )  )
class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    None
					_SCREAMING_SNAKE_CASE							   =    None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  List[str]       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											with TemporaryDirectory() as tmp_dir:
																	_snake_case:   List[str]  					=		dataset_module_factory(__snake_case       ,     cache_dir=__snake_case       )

																	_snake_case:   Dict  					=		import_main_class(dataset_module.module_path       ,     dataset=__snake_case       )

																	_snake_case:   DatasetBuilder  					=		builder_cls(
																	    cache_dir=__snake_case       ,     config_name=__snake_case       ,     hash=dataset_module.hash       ,     )

																	_snake_case:   List[str]  					=		'/'.join(
																	    [
																	        HF_GCP_BASE_URL,
																	        builder_instance._relative_data_dir(with_hash=__snake_case       ).replace(os.sep       ,     '/'       ),
																	        config.DATASET_INFO_FILENAME,
																	    ]       )
																	_snake_case:   int  					=		cached_path(__snake_case       ,     cache_dir=__snake_case       )
																	self.assertTrue(os.path.exists(__snake_case       )       )





@pytest.mark.integration
def       lowercase_      (		lowercase__						)   ->List[Any]:
						_snake_case:   Tuple  					=		tmp_path_factory.mktemp('test_hf_gcp'						) / 'test_wikipedia_simple'
						_snake_case:   Tuple  					=		dataset_module_factory('wikipedia'				,  cache_dir=lowercase__						)
						_snake_case:   List[str]  					=		import_main_class(dataset_module.module_path						)
						_snake_case:   DatasetBuilder  					=		builder_cls(
						    cache_dir=lowercase__				,  config_name='20220301.frr'				,  hash=dataset_module.hash				,  )
						# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
						_snake_case:   int  					=		None
						builder_instance.download_and_prepare()
						_snake_case:   Optional[Any]  					=		builder_instance.as_dataset()
						assert ds





@pytest.mark.integration
def       lowercase_      (		lowercase__						)   ->Tuple:
						_snake_case:   List[Any]  					=		dataset_module_factory('wikipedia'				,  cache_dir=lowercase__						)
						_snake_case:   Optional[int]  					=		import_main_class(dataset_module.module_path				,  dataset=lowercase__						)
						_snake_case:   DatasetBuilder  					=		builder_cls(
						    cache_dir=lowercase__				,  config_name='20220301.frr'				,  hash=dataset_module.hash				,  )
						_snake_case:   str  					=		builder_instance.as_streaming_dataset()
						assert ds
						assert isinstance(lowercase__				,  lowercase__						)
						assert "train" in ds
						assert isinstance(ds['train']				,  lowercase__						)
						assert next(iter(ds['train']						)						)












'''simple docstring'''






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


A   : Tuple             =			['bert-base-uncased', 'bert-base-cased']
A   : str             =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







		class 			lowerCamelCase					(						tf.keras.Model  ):


							def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ):





													'''simple docstring'''





													super().__init__()
													_snake_case:   List[Any]  					=		tokenizer
													_snake_case:   str  					=		AutoConfig.from_pretrained(__snake_case       )
													_snake_case:   List[Any]  					=		TFAutoModel.from_config(__snake_case       )




							def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  List[str]       ):





													'''simple docstring'''





													_snake_case:   Optional[int]  					=		self.tokenizer(__snake_case       )
													_snake_case:   Tuple  					=		self.bert(**__snake_case       )
													return out["pooler_output"]







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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   List[Any]  					=		[
											    BertTokenizer.from_pretrained(__snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_snake_case:   List[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       )

											_snake_case:   int  					=		[
											    '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',
											]
											_snake_case:   str  					=		list(zip(self.test_sentences       ,     self.test_sentences[::-1]       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											for tokenizer, tf_tokenizer in zip(self.tokenizers       ,     self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   List[Any]  					=		tokenizer(__snake_case       ,     return_tensors='tf'       ,     padding='longest'       )
																							_snake_case:   Optional[Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   str  					=		tf_tokenizer(self.paired_sentences       )
																	_snake_case:   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    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   List[str]  					=		tf.function(__snake_case       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   Union[str, Any]  					=		tf.constant(__snake_case       )
																							_snake_case:   Any  					=		compiled_tokenizer(__snake_case       )
																							_snake_case:   Union[str, Any]  					=		tf_tokenizer(__snake_case       )

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




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   Optional[Any]  					=		ModelToSave(tokenizer=__snake_case       )
																	_snake_case:   Tuple  					=		tf.convert_to_tensor(self.test_sentences       )
																	_snake_case:   List[str]  					=		model(__snake_case       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							_snake_case:   List[str]  					=		Path(__snake_case       ) / 'saved.model'
																							model.save(__snake_case       )
																							_snake_case:   int  					=		tf.keras.models.load_model(__snake_case       )
																	_snake_case:   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       )





