






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 :      Optional[int]										=				get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")



@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__						(    _lowerCamelCase			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		SpeechTaTokenizer
					a_     =		False
					a_     =		True



					def 	_lowercase					( self					:  List[Any]	):
						super().setUp()

						# We have a SentencePiece fixture for testing
						snake_case__			:				int       =  SpeechTaTokenizer(lowercase_	)

						snake_case__			:				Any       =  AddedToken("<mask>"							,		lstrip=lowercase_							,		rstrip=lowercase_	)
						snake_case__			:				Dict       =  mask_token
						tokenizer.add_special_tokens({"mask_token": mask_token}	)
						tokenizer.add_tokens(["<ctc_blank>"]	)

						tokenizer.save_pretrained(self.tmpdirname	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[str]	):
						snake_case__			:				List[Any]       =  "this is a test"
						snake_case__			:				int       =  "this is a test"
						return input_text, output_text



					def 	_lowercase					( self					:  List[Any]							,		__A					:  Union[str, Any]							,		__A					:  Tuple=False							,		__A					:  int=2_0							,		__A					:  Dict=5	):
						snake_case__,						snake_case__			:				Optional[Any]       =  self.get_input_output_texts(lowercase_	)
						snake_case__			:				Optional[int]       =  tokenizer.encode(lowercase_							,		add_special_tokens=lowercase_	)
						snake_case__			:				str       =  tokenizer.decode(lowercase_							,		clean_up_tokenization_spaces=lowercase_	)
						return text, ids



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  "<pad>"
						snake_case__			:				Union[str, Any]       =  1

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



					def 	_lowercase					( self					:  str	):
						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[-4]							,		"œ"	)
						self.assertEqual(vocab_keys[-2]							,		"<mask>"	)
						self.assertEqual(vocab_keys[-1]							,		"<ctc_blank>"	)
						self.assertEqual(len(lowercase_	)							,		8_1	)



					def 	_lowercase					( self					:  Tuple	):
						self.assertEqual(self.get_tokenizer().vocab_size							,		7_9	)



					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Dict       =  self.get_tokenizers(do_lower_case=lowercase_	)
						for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
								snake_case__			:				int       =  tokenizer.vocab_size
								snake_case__			:				List[str]       =  len(lowercase_	)

								self.assertNotEqual(lowercase_							,		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)

								snake_case__			:				Union[str, Any]       =  ["aaaaa bbbbbb", "cccccccccdddddddd"]
								snake_case__			:				Optional[int]       =  tokenizer.add_tokens(lowercase_	)
								snake_case__			:				Dict       =  tokenizer.vocab_size
								snake_case__			:				Any       =  len(lowercase_	)

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

								snake_case__			:				str       =  tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"							,		add_special_tokens=lowercase_	)

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

								snake_case__			:				Tuple       =  {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
								snake_case__			:				str       =  tokenizer.add_special_tokens(lowercase_	)
								snake_case__			:				List[str]       =  tokenizer.vocab_size
								snake_case__			:				int       =  len(lowercase_	)

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

								snake_case__			:				int       =  tokenizer.encode(
								    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"							,		add_special_tokens=lowercase_	)

								self.assertGreaterEqual(len(lowercase_	)							,		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 	_lowercase					( self					:  Tuple	):
						pass



					def 	_lowercase					( self					:  str	):
						pass



					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.get_tokenizer()

						snake_case__			:				Union[str, Any]       =  tokenizer.tokenize("This is a test"	)
						# fmt: off
						self.assertListEqual(lowercase_							,		[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(lowercase_	)							,		[4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]							,		)

						snake_case__			:				str       =  tokenizer.tokenize("I was born in 92000, and this is falsé."	)
						self.assertListEqual(
						    lowercase_							,		[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", "."]	)

						snake_case__			:				List[str]       =  tokenizer.convert_tokens_to_ids(lowercase_	)
						# fmt: off
						self.assertListEqual(lowercase_							,		[4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]	)
						# fmt: on

						snake_case__			:				str       =  tokenizer.convert_ids_to_tokens(lowercase_	)
						self.assertListEqual(
						    lowercase_							,		[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 	_lowercase					( self					:  List[str]	):
						# Use custom sequence because this tokenizer does not handle numbers.
						snake_case__			:				int       =  [
						    "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
						snake_case__			:				Optional[int]       =  {
						    "input_ids": [
						        [4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
						        [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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],
						        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    ]
						}
						# fmt: on

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









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
	)
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--hf_config""",
	    default="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



