



"""simple docstring"""







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class      UpperCAmelCase__		(      yaml.SafeLoader     ):



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    Optional[int]				)			->						Any:






									'''simple docstring'''

									A         =					[self.constructed_objects[key_node] for key_node, _ in node.value]
									A         =					[tuple(snake_case				) if isinstance(snake_case      ,							snake_case				) else key for key in keys]
									A         =					Counter(snake_case				)
									A         =					[key for key in counter if counter[key] > 1]
									if duplicate_keys:
																raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""				)



		def 					A_		( self  :    Any      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Any=False				)			->						Tuple:






									'''simple docstring'''

									A         =					super().construct_mapping(snake_case      ,							deep=snake_case				)
									self._check_no_duplicates_on_constructed_node(snake_case				)
									return mapping



def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Tuple[Optional[str], str]:
							A         =					list(readme_content.splitlines()			)
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														A         =					full_content[1:].index('---'			) + 1
														A         =					'\n'.join(full_content[1:sep_idx]			)
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]			)

							return None, "\n".join(lowerCamelCase__			)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		# class attributes
		lowerCAmelCase_       :	int 							=     {"""train_eval_index"""}  # train-eval-index in the YAML metadata



		@classmethod
		def 					A_		( cls  :    Dict      ,							snake_case  :    Path				)			->						"DatasetMetadata":






									'''simple docstring'''

									with open(snake_case      ,							encoding='utf-8'				) as readme_file:
																A      ,  A         =					_split_yaml_from_readme(readme_file.read()				)
									if yaml_string is not None:
																return cls.from_yaml_string(snake_case				)
									else:
																return cls()



		def 					A_		( self  :    Any      ,							snake_case  :    Path				)			->						Union[str, Any]:






									'''simple docstring'''

									if path.exists():
																with open(snake_case      ,							encoding='utf-8'				) as readme_file:
																							A         =					readme_file.read()
									else:
																A         =					None
									A         =					self._to_readme(snake_case				)
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as readme_file:
																readme_file.write(snake_case				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Optional[str] = None				)			->						str:






									'''simple docstring'''

									if readme_content is not None:
																A      ,  A         =					_split_yaml_from_readme(snake_case				)
																A         =					'---\n' + self.to_yaml_string() + '---\n' + content
									else:
																A         =					'---\n' + self.to_yaml_string() + '---\n'
									return full_content



		@classmethod
		def 					A_		( cls  :    Optional[int]      ,							snake_case  :    str				)			->						"DatasetMetadata":






									'''simple docstring'''

									A         =					yaml.load(snake_case      ,							Loader=_NoDuplicateSafeLoader				) or {}

									# Convert the YAML keys to DatasetMetadata fields
									A         =					{
									    (key.replace('-'      ,							'_'				) if key.replace('-'      ,							'_'				) in cls._FIELDS_WITH_DASHES else key): value
									    for key, value in metadata_dict.items()
									}
									return cls(**snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									return yaml.safe_dump(
									    {
									        (key.replace('_'      ,							'-'				) if key in self._FIELDS_WITH_DASHES else key): value
									        for key, value in self.items()
									    }      ,							sort_keys=snake_case      ,							allow_unicode=snake_case      ,							encoding='utf-8'      ,							).decode('utf-8'				)



A    =			{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
							from argparse import ArgumentParser

							A    =			ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
							ap.add_argument('readme_filepath')
							A    =			ap.parse_args()

							A    =			Path(args.readme_filepath)
							A    =			DatasetMetadata.from_readme(readme_filepath)
							print(dataset_metadata)
							dataset_metadata.to_readme(readme_filepath)








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

if is_tf_available():






							class      UpperCAmelCase__		(      tf.keras.Model     ):



									def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]				)			->						List[str]:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFAutoModel.from_config(snake_case				)



									def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						Dict:






																'''simple docstring'''

																A         =					self.tokenizer(snake_case				)
																A         =					self.bert(**snake_case				)
																return out["pooler_output"]






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



		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    BertTokenizer.from_pretrained(snake_case				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									A         =					[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				)

									A         =					[
									    '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',
									]
									A         =					list(zip(self.test_sentences      ,							self.test_sentences[::-1]				)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,							self.tf_tokenizers				):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tokenizer(snake_case      ,							return_tensors='tf'      ,							padding='longest'				)
																							A         =					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  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf_tokenizer(self.paired_sentences				)
																A         =					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  :    Any				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					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[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor(self.test_sentences				)
																A         =					model(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							model.save(snake_case				)
																							A         =					tf.keras.models.load_model(snake_case				)
																A         =					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				)



