

import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCAmelCase__								=		pytest.mark.integration






@pytest.mark.parametrize('path'							,       ['paws', 'csv']	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	inspect_dataset(lowerCamelCase_							,       lowerCamelCase_	)
	_UpperCamelCase   :		str			=						path + '.py'
	assert script_name in os.listdir(lowerCamelCase_	)
	assert "__pycache__" not in os.listdir(lowerCamelCase_	)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'	)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'	)
@pytest.mark.parametrize('path'							,       ['accuracy']	)
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	inspect_metric(lowerCamelCase_							,       lowerCamelCase_	)
	_UpperCamelCase   :		int			=						path + '.py'
	assert script_name in os.listdir(lowerCamelCase_	)
	assert "__pycache__" not in os.listdir(lowerCamelCase_	)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'							,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Union[str, Any]	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						get_dataset_config_info(lowerCamelCase_							,       config_name=lowerCamelCase_	)
	assert info.config_name == config_name
	assert list(info.splits.keys()	) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'							,       [
        ('paws', None, ValueError),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Any	)						->				Optional[int]:




	'''simple docstring'''




	with pytest.raises(lowerCamelCase_	):
		get_dataset_config_info(lowerCamelCase_							,       config_name=lowerCamelCase_	)






@pytest.mark.parametrize(
    'path, expected'							,       [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Any	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						get_dataset_config_names(lowerCamelCase_	)
	assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'							,       [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     str	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						get_dataset_infos(lowerCamelCase_	)
	assert list(infos.keys()	) == expected_configs
	_UpperCamelCase   :		Tuple			=						expected_configs[0]
	assert expected_config in infos
	_UpperCamelCase   :		List[Any]			=						infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()	) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'							,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Tuple	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						get_dataset_infos(lowerCamelCase_	)
	assert expected_config in infos
	_UpperCamelCase   :		Union[str, Any]			=						infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()	) == expected_splits







@pytest.mark.parametrize(
    'path, config_name, expected_exception'							,       [
        ('paws', None, ValueError),
    ]							,       )
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]	)						->				Tuple:




	'''simple docstring'''




	with pytest.raises(lowerCamelCase_	):
		get_dataset_split_names(lowerCamelCase_							,       config_name=lowerCamelCase_	)









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)




