






'''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


_UpperCamelCase						:						Union[str, Any]  =					['''bert-base-uncased''', '''bert-base-cased''']
_UpperCamelCase						:						Optional[int]  =					'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():


						class  snake_case__      (					tf.keras.Model):




											def __init__(  self  :		str				, _A  :		Union[str, Any]      )    ->							int:
														super().__init__()
														UpperCAmelCase_      :       Any          =       tokenizer
														UpperCAmelCase_      :       Any          =       AutoConfig.from_pretrained(snake_case__      )
														UpperCAmelCase_      :       str          =       TFAutoModel.from_config(snake_case__      )





											def 		A      (  self  :		Any				, _A  :		Tuple      )    ->							Tuple:
														UpperCAmelCase_      :       Optional[int]          =       self.tokenizer(snake_case__      )
														UpperCAmelCase_      :       str          =       self.bert(**snake_case__      )
														return out["pooler_output"]


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




					def 		A      (  self  :		str      )    ->							int:
								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_      :       Dict          =       [
								    '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  :		Optional[int]      )    ->							Optional[int]:
								for tokenizer, tf_tokenizer in zip(self.tokenizers				, self.tf_tokenizers      ):
											for test_inputs in (self.test_sentences, self.paired_sentences):
														UpperCAmelCase_      :       Optional[int]          =       tokenizer(snake_case__				, return_tensors='''tf'''				, padding='''longest'''      )
														UpperCAmelCase_      :       Union[str, 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 		A      (  self  :		List[Any]      )    ->							Union[str, Any]:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       int          =       tf_tokenizer(self.paired_sentences      )
											UpperCAmelCase_      :       List[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  :		Optional[int]      )    ->							List[str]:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       str          =       tf.function(snake_case__      )
											for test_inputs in (self.test_sentences, self.paired_sentences):
														UpperCAmelCase_      :       List[Any]          =       tf.constant(snake_case__      )
														UpperCAmelCase_      :       Union[str, Any]          =       compiled_tokenizer(snake_case__      )
														UpperCAmelCase_      :       Dict          =       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  :		Optional[int]      )    ->							List[str]:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       int          =       ModelToSave(tokenizer=snake_case__      )
											UpperCAmelCase_      :       Union[str, Any]          =       tf.convert_to_tensor(self.test_sentences      )
											UpperCAmelCase_      :       List[Any]          =       model(snake_case__      )  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
														UpperCAmelCase_      :       Any          =       Path(snake_case__      ) / 'saved.model'
														model.save(snake_case__      )
														UpperCAmelCase_      :       Any          =       tf.keras.models.load_model(snake_case__      )
											UpperCAmelCase_      :       Tuple          =       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      )








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,
)


UpperCamelCase__ :   List[Any]						=							pytest.mark.integration





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







			"""simple docstring"""





			inspect_dataset(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[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        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  Optional[Any]:







			"""simple docstring"""





			inspect_metric(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     int  )       ->  List[Any]:







			"""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        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_infos(lowerCamelCase_  )
			assert list(infos.keys()  ) == expected_configs
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : 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        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_dataset_infos(lowerCamelCase_  )
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Tuple					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





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



