



import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase   				=			get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')




@require_sentencepiece
@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					SpeechTaTokenizer
							lowerCamelCase_           =					False
							lowerCamelCase_           =					True


							def      _snake_case  (							self    :Tuple		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE__			=      SpeechTaTokenizer(__A		)

									SCREAMING_SNAKE_CASE__			=      AddedToken("""<mask>"""							,				lstrip=__A							,				rstrip=__A		)
									SCREAMING_SNAKE_CASE__			=      mask_token
									tokenizer.add_special_tokens({"""mask_token""": mask_token}		)
									tokenizer.add_tokens(["""<ctc_blank>"""]		)

									tokenizer.save_pretrained(self.tmpdirname		)


							def      _snake_case  (							self    :int							,				__A    :int		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """this is a test"""
									SCREAMING_SNAKE_CASE__			=      """this is a test"""
									return input_text, output_text


							def      _snake_case  (							self    :Tuple							,				__A    :Optional[Any]							,				__A    :Union[str, Any]=False							,				__A    :List[str]=20							,				__A    :List[str]=5		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.get_input_output_texts(__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.decode(__A							,				clean_up_tokenization_spaces=__A		)
									return text, ids


							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """<pad>"""
									SCREAMING_SNAKE_CASE__			=      1

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


							def      _snake_case  (							self    :int		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]							,				"""<s>"""		)
									self.assertEqual(vocab_keys[1]							,				"""<pad>"""		)
									self.assertEqual(vocab_keys[-4]							,				"""œ"""		)
									self.assertEqual(vocab_keys[-2]							,				"""<mask>"""		)
									self.assertEqual(vocab_keys[-1]							,				"""<ctc_blank>"""		)
									self.assertEqual(len(__A		)							,				81		)


							def      _snake_case  (							self    :Any		)   ->   str:



									"""simple docstring"""



									self.assertEqual(self.get_tokenizer().vocab_size							,				79		)


							def      _snake_case  (							self    :Dict		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizers(do_lower_case=__A		)
									for tokenizer in tokenizers:
											with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
													SCREAMING_SNAKE_CASE__			=      tokenizer.vocab_size
													SCREAMING_SNAKE_CASE__			=      len(__A		)

													self.assertNotEqual(__A							,				0		)

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													SCREAMING_SNAKE_CASE__			=      ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
													SCREAMING_SNAKE_CASE__			=      tokenizer.add_tokens(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.vocab_size
													SCREAMING_SNAKE_CASE__			=      len(__A		)

													self.assertNotEqual(__A							,				0		)
													self.assertEqual(__A							,				__A		)
													self.assertEqual(__A							,				len(__A		)		)
													self.assertEqual(__A							,				all_size + len(__A		)		)

													SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""							,				add_special_tokens=__A		)

													self.assertGreaterEqual(len(__A		)							,				4		)
													self.assertGreater(tokens[0]							,				tokenizer.vocab_size - 1		)
													self.assertGreater(tokens[-3]							,				tokenizer.vocab_size - 1		)

													SCREAMING_SNAKE_CASE__			=      {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
													SCREAMING_SNAKE_CASE__			=      tokenizer.add_special_tokens(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.vocab_size
													SCREAMING_SNAKE_CASE__			=      len(__A		)

													self.assertNotEqual(__A							,				0		)
													self.assertEqual(__A							,				__A		)
													self.assertEqual(__A							,				len(__A		)		)
													self.assertEqual(__A							,				all_size_a + len(__A		)		)

													SCREAMING_SNAKE_CASE__			=      tokenizer.encode(
													    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""							,				add_special_tokens=__A		)

													self.assertGreaterEqual(len(__A		)							,				6		)
													self.assertGreater(tokens[0]							,				tokenizer.vocab_size - 1		)
													self.assertGreater(tokens[0]							,				tokens[1]		)
													self.assertGreater(tokens[-3]							,				tokenizer.vocab_size - 1		)
													self.assertGreater(tokens[-3]							,				tokens[-4]		)
													self.assertEqual(tokens[0]							,				tokenizer.eos_token_id		)
													self.assertEqual(tokens[-3]							,				tokenizer.pad_token_id		)


							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :Optional[Any]		)   ->   Dict:



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :Union[str, Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize("""This is a test"""		)
									# fmt: off
									self.assertListEqual(__A							,				[SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""]		)
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__A		)							,				[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]							,				)

									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
									self.assertListEqual(
									    __A							,				[SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_ids(__A		)
									# fmt: off
									self.assertListEqual(__A							,				[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]		)
									# fmt: on

									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(__A		)
									self.assertListEqual(
									    __A							,				[SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]		)


							@slow
							def      _snake_case  (							self    :Optional[int]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      [
									    """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
									    """general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
									    """Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
									    """models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
									    """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
									    """conditioning on both left and right context in all layers.""",
									    """The quick brown fox jumps over the lazy dog.""",
									]

									# fmt: off
									SCREAMING_SNAKE_CASE__			=      {
									    """input_ids""": [
									        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
									        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 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],
									        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 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, 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],
									    ],
									    """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, 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, 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, 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, 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, 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],
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									    ]
									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__A							,				model_name="""microsoft/speecht5_asr"""							,				revision="""c5ef64c71905caeccde0e4462ef3f9077224c524"""							,				sequences=__A							,				)






from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		return {key.lstrip("""-"""    ): value for key, value in zip(unknown_args[::2]					,   unknown_args[1::2]    )}






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      ArgumentParser(
		    """HuggingFace Datasets CLI tool"""					,   usage="""datasets-cli <command> [<args>]"""					,   allow_abbrev=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      parser.add_subparsers(help="""datasets-cli command helpers"""    )
		set_verbosity_info()

		# Register commands
		ConvertCommand.register_subcommand(UpperCamelCase__    )
		EnvironmentCommand.register_subcommand(UpperCamelCase__    )
		TestCommand.register_subcommand(UpperCamelCase__    )
		RunBeamCommand.register_subcommand(UpperCamelCase__    )
		DummyDataCommand.register_subcommand(UpperCamelCase__    )

		# Parse args
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      parser.parse_known_args()
		if not hasattr(UpperCamelCase__					,   """func"""    ):
				parser.print_help()
				exit(1    )
		SCREAMING_SNAKE_CASE__			=      parse_unknown_args(UpperCamelCase__    )

		# Run
		SCREAMING_SNAKE_CASE__			=      args.func(UpperCamelCase__					,   **UpperCamelCase__    )
		service.run()


if __name__ == "__main__":
	main()