import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase					:	Tuple       =  get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							XLMRobertaTokenizer
					A	       =							XLMRobertaTokenizerFast
					A	       =							True
					A	       =							True

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().setUp()

							# We have a SentencePiece fixture for testing
							SCREAMING_SNAKE_CASE_  :		int				      =    XLMRobertaTokenizer(_A,keep_accents=_A    )
							tokenizer.save_pretrained(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "<pad>"
							SCREAMING_SNAKE_CASE_  :		Dict				      =    1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A    ),_A    )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A    ),_A    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    list(self.get_tokenizer().get_vocab().keys()    )

							self.assertEqual(vocab_keys[0],"<s>"    )
							self.assertEqual(vocab_keys[1],"<pad>"    )
							self.assertEqual(vocab_keys[-1],"<mask>"    )
							self.assertEqual(len(_A    ),1002    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size,1002    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    XLMRobertaTokenizer(_A,keep_accents=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer.tokenize("This is a test"    )
							self.assertListEqual(_A,["▁This", "▁is", "▁a", "▁t", "est"]    )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(_A    ),[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],)

							SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer.tokenize("I was born in 92000, and this is falsé."    )
							self.assertListEqual(
							    _A,[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "9",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "é",
							        ".",
							    ],)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer.convert_tokens_to_ids(_A    )
							self.assertListEqual(
							    _A,[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ],)

							SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer.convert_ids_to_tokens(_A    )
							self.assertListEqual(
							    _A,[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "<unk>",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "<unk>",
							        ".",
							    ],)

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
											SCREAMING_SNAKE_CASE_  :		str				      =    self.rust_tokenizer_class.from_pretrained(_A,**_A    )
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.tokenizer_class.from_pretrained(_A,**_A    )

											SCREAMING_SNAKE_CASE_  :		int				      =    tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE_  :		List[str]				      =    tokenizer_r.save_pretrained(_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_p.save_pretrained(_A    )

											# Checks it save with the same files + the tokenizer.json file for the fast one
											self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files    )    )
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f    )
											self.assertSequenceEqual(_A,_A    )

											# Checks everything loads correctly in the same way
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tokenizer_r.from_pretrained(_A    )
											SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer_p.from_pretrained(_A    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_A,_A    )    )
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

											shutil.rmtree(_A    )

											# Save tokenizer rust, legacy_format=True
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_r.save_pretrained(_A,legacy_format=_A    )
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer_p.save_pretrained(_A    )

											# Checks it save with the same files
											self.assertSequenceEqual(_A,_A    )

											# Checks everything loads correctly in the same way
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer_r.from_pretrained(_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_p.from_pretrained(_A    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_A,_A    )    )

											shutil.rmtree(_A    )

											# Save tokenizer rust, legacy_format=False
											SCREAMING_SNAKE_CASE_  :		Dict				      =    tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_r.save_pretrained(_A,legacy_format=_A    )
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer_p.save_pretrained(_A    )

											# Checks it saved the tokenizer.json file
											self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files    )    )

											# Checks everything loads correctly in the same way
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer_r.from_pretrained(_A    )
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tokenizer_p.from_pretrained(_A    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_A,_A    )    )

											shutil.rmtree(_A    )

					@cached_property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							with tempfile.NamedTemporaryFile() as f:
									shutil.copyfile(_A,f.name    )
									SCREAMING_SNAKE_CASE_  :		Dict				      =    XLMRobertaTokenizer(f.name,keep_accents=_A    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pickle.dumps(_A    )
							pickle.loads(_A    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							if not self.test_rust_tokenizer:
									return

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_rust_tokenizer()

							SCREAMING_SNAKE_CASE_  :		int				      =    "I was born in 92000, and this is falsé."

							SCREAMING_SNAKE_CASE_  :		str				      =    tokenizer.tokenize(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    rust_tokenizer.tokenize(_A    )
							self.assertListEqual(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer.encode(_A,add_special_tokens=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    rust_tokenizer.encode(_A,add_special_tokens=_A    )
							self.assertListEqual(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer.encode(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    rust_tokenizer.encode(_A    )
							self.assertListEqual(_A,_A    )

					@slow
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "Hello World!"
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [0, 3_5378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_A,self.big_tokenizer.encode(_A    )    )

					@slow
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (
							    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
							    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
							)
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    17_9459,
							    12_4850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    1_0114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    2_2376,
							    642,
							    1221,
							    1_5190,
							    3_4153,
							    450,
							    5608,
							    959,
							    1119,
							    5_7702,
							    136,
							    186,
							    47,
							    1098,
							    2_9367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6044,
							    237,
							    6284,
							    5_0901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_A,self.big_tokenizer.encode(_A    )    )






					@slow
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_A,model_name="xlm-roberta-base",revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",)

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

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [self.constructed_objects[key_node] for key_node, _ in node.value]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(_A    ) if isinstance(_A,_A    ) else key for key in keys]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Counter(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
									raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    list(readme_content.splitlines()	)
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    full_content[1:].index("---"	) + 1
				SCREAMING_SNAKE_CASE_  :		int				      =    "\n".join(full_content[1:sep_idx]	)
				return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

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






class 		a__						(					A__					):
					# class attributes
					A	       =							{'train_eval_index'}  # train-eval-index in the YAML metadata

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






							with open(_A,encoding="utf-8"    ) as readme_file:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    _split_yaml_from_readme(readme_file.read()    )
							if yaml_string is not None:
									return cls.from_yaml_string(_A    )
							else:
									return cls()

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






							if path.exists():
									with open(_A,encoding="utf-8"    ) as readme_file:
											SCREAMING_SNAKE_CASE_  :		int				      =    readme_file.read()
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		int				      =    self._to_readme(_A    )
							with open(_A,"w",encoding="utf-8"    ) as readme_file:
									readme_file.write(_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

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






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""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=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	List[Any]       =  {
    '''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

		__lowerCamelCase					:	List[Any]       =  ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		__lowerCamelCase					:	Dict       =  ap.parse_args()

		__lowerCamelCase					:	List[Any]       =  Path(args.readme_filepath)
		__lowerCamelCase					:	Optional[int]       =  DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)
