

'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__										= get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
						from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCAmelCase__										= 250004
lowerCAmelCase__										= 250020



@require_sentencepiece
@require_tokenizers
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      List[Any]=   MBartaaTokenizer
							_lowerCamelCase		:      List[Any]=   MBartaaTokenizerFast
							_lowerCamelCase		:      Optional[Any]=   True
							_lowerCamelCase		:      List[str]=   True


							def 						_snake_case							(			self)							->				List[Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase_		:      Optional[Any]       	=					MBartaaTokenizer(_snake_case    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=_snake_case)
								tokenizer.save_pretrained(self.tmpdirname)


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Dict       	=					'<s>'
								UpperCAmelCase_		:      Tuple       	=					0

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


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      Tuple       	=					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(_snake_case)    ,			1054)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								self.assertEqual(self.get_tokenizer().vocab_size    ,			1054)


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					MBartaaTokenizer(_snake_case    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=_snake_case)

								UpperCAmelCase_		:      int       	=					tokenizer.tokenize('This is a test')
								self.assertListEqual(_snake_case    ,			['▁This', '▁is', '▁a', '▁t', 'est'])

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

								UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.tokenize('I was born in 92000, and this is falsé.')
								self.assertListEqual(
								    _snake_case    ,			[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', '.']    ,			)
								UpperCAmelCase_		:      str       	=					tokenizer.convert_tokens_to_ids(_snake_case)
								self.assertListEqual(
								    _snake_case    ,			[
								        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]
								    ]    ,			)

								UpperCAmelCase_		:      List[str]       	=					tokenizer.convert_ids_to_tokens(_snake_case)
								self.assertListEqual(
								    _snake_case    ,			[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>', '.']    ,			)


							@slow
							def 						_snake_case							(			self)							->				Union[str, Any]:
								# fmt: off
								UpperCAmelCase_		:      Union[str, Any]       	=					{'input_ids': [[25_0004, 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], [25_0004, 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], [25_0004, 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=_snake_case    ,			model_name='facebook/mbart-large-50'    ,			revision='d3913889c59cd5c9e456b269c376325eabad57e2'    ,			)





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

								UpperCAmelCase_		:      Tuple       	=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      str       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)
										UpperCAmelCase_		:      Any       	=					self.tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      Dict       	=					tempfile.mkdtemp()

										UpperCAmelCase_		:      Tuple       	=					tokenizer_r.save_pretrained(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer_p.save_pretrained(_snake_case)

										# 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))
										UpperCAmelCase_		:      Tuple       	=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
										self.assertSequenceEqual(_snake_case    ,			_snake_case)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer_r.from_pretrained(_snake_case)
										UpperCAmelCase_		:      List[Any]       	=					tokenizer_p.from_pretrained(_snake_case)

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

										shutil.rmtree(_snake_case)

										# Save tokenizer rust, legacy_format=True
										UpperCAmelCase_		:      str       	=					tempfile.mkdtemp()

										UpperCAmelCase_		:      List[str]       	=					tokenizer_r.save_pretrained(_snake_case    ,			legacy_format=_snake_case)
										UpperCAmelCase_		:      List[str]       	=					tokenizer_p.save_pretrained(_snake_case)

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

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

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

										shutil.rmtree(_snake_case)

										# Save tokenizer rust, legacy_format=False
										UpperCAmelCase_		:      Any       	=					tempfile.mkdtemp()

										UpperCAmelCase_		:      List[str]       	=					tokenizer_r.save_pretrained(_snake_case    ,			legacy_format=_snake_case)
										UpperCAmelCase_		:      List[str]       	=					tokenizer_p.save_pretrained(_snake_case)

										# 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
										UpperCAmelCase_		:      Any       	=					tokenizer_r.from_pretrained(_snake_case)
										UpperCAmelCase_		:      Dict       	=					tokenizer_p.from_pretrained(_snake_case)

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

										shutil.rmtree(_snake_case)



@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      List[Any]=   "facebook/mbart-large-50-one-to-many-mmt"
							_lowerCamelCase		:      Any=   [
							    " UN Chief Says There Is No Military Solution in Syria",
							    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
							]
							_lowerCamelCase		:      Optional[Any]=   [
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
							    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
							    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
							]
							_lowerCamelCase		:      str=   [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]


							@classmethod
							def 						_snake_case							(			cls)							->				Dict:
								UpperCAmelCase_		:      MBartaaTokenizer       	=					MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO')
								UpperCAmelCase_		:      Tuple       	=					1
								return cls


							def 						_snake_case							(			self)							->				Union[str, Any]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']    ,			25_0001)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']    ,			25_0004)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']    ,			25_0020)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']    ,			25_0038)


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens    ,			_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								self.assertIn(_snake_case    ,			self.tokenizer.all_special_ids)
								UpperCAmelCase_		:      Tuple       	=					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								UpperCAmelCase_		:      Dict       	=					self.tokenizer.decode(_snake_case    ,			skip_special_tokens=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					self.tokenizer.decode(generated_ids[1:]    ,			skip_special_tokens=_snake_case)
								self.assertEqual(_snake_case    ,			_snake_case)
								self.assertNotIn(self.tokenizer.eos_token    ,			_snake_case)


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					10
								UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_snake_case    ,			max_length=_snake_case    ,			truncation=_snake_case).input_ids[0]
								self.assertEqual(ids[0]    ,			_snake_case)
								self.assertEqual(ids[-1]    ,			2)
								self.assertEqual(len(_snake_case)    ,			_snake_case)


							def 						_snake_case							(			self)							->				Tuple:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])    ,			[25_0053, 25_0001])


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[int]       	=					tempfile.mkdtemp()
								UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_snake_case)
								UpperCAmelCase_		:      int       	=					MBartaaTokenizer.from_pretrained(_snake_case)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,			_snake_case)


							@require_torch
							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					self.tokenizer(self.src_text    ,			text_target=self.tgt_text    ,			padding=_snake_case    ,			return_tensors='pt')
								UpperCAmelCase_		:      List[Any]       	=					shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == RO_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]


							@require_torch
							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.tokenizer(
								    self.src_text    ,			text_target=self.tgt_text    ,			padding=_snake_case    ,			truncation=_snake_case    ,			max_length=len(self.expected_src_tokens)    ,			return_tensors='pt'    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id)

								self.assertIsInstance(_snake_case    ,			_snake_case)

								self.assertEqual((2, 14)    ,			batch.input_ids.shape)
								self.assertEqual((2, 14)    ,			batch.attention_mask.shape)
								UpperCAmelCase_		:      Optional[int]       	=					batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens    ,			_snake_case)
								self.assertEqual(2    ,			batch.decoder_input_ids[0, 0])  # decoder_start_token_id
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens    ,			[EN_CODE])
								self.assertEqual(self.tokenizer.suffix_tokens    ,			[self.tokenizer.eos_token_id])


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      int       	=					self.tokenizer(self.src_text    ,			padding=_snake_case    ,			truncation=_snake_case    ,			max_length=3    ,			return_tensors='pt')
								UpperCAmelCase_		:      Union[str, Any]       	=					self.tokenizer(
								    text_target=self.tgt_text    ,			padding=_snake_case    ,			truncation=_snake_case    ,			max_length=10    ,			return_tensors='pt')
								UpperCAmelCase_		:      Dict       	=					targets['input_ids']
								UpperCAmelCase_		:      Optional[int]       	=					shift_tokens_right(_snake_case    ,			self.tokenizer.pad_token_id)

								self.assertEqual(batch.input_ids.shape[1]    ,			3)
								self.assertEqual(batch.decoder_input_ids.shape[1]    ,			10)





							@require_torch
							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Any       	=					self.tokenizer._build_translation_inputs(
								    'A test'    ,			return_tensors='pt'    ,			src_lang='en_XX'    ,			tgt_lang='ar_AR')

								self.assertEqual(
								    nested_simplify(_snake_case)    ,			{
								        # en_XX, A, test, EOS
								        'input_ids': [[25_0004, 62, 3034, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 25_0001,
								    }    ,			)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "gptsan-japanese"
							_lowerCamelCase		:      List[Any]=   [
							    "past_key_values",
							]
							_lowerCamelCase		:      Tuple=   {
							    "hidden_size": "d_model",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=3_6000    ,			_snake_case=1280    ,			_snake_case=1024    ,			_snake_case=8192    ,			_snake_case=4096    ,			_snake_case=128    ,			_snake_case=10    ,			_snake_case=0    ,			_snake_case=16    ,			_snake_case=16    ,			_snake_case=128    ,			_snake_case=0.0    ,			_snake_case=1e-5    ,			_snake_case=False    ,			_snake_case=0.0    ,			_snake_case="float32"    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=0.002    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=3_5998    ,			_snake_case=3_5995    ,			_snake_case=3_5999    ,			**_snake_case    ,			)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					vocab_size
								UpperCAmelCase_		:      Optional[int]       	=					max_position_embeddings
								UpperCAmelCase_		:      Any       	=					d_model
								UpperCAmelCase_		:      int       	=					d_ff
								UpperCAmelCase_		:      Tuple       	=					d_ext
								UpperCAmelCase_		:      Tuple       	=					d_spout
								UpperCAmelCase_		:      List[Any]       	=					num_switch_layers
								UpperCAmelCase_		:      Tuple       	=					num_ext_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_switch_layers + num_ext_layers
								UpperCAmelCase_		:      Tuple       	=					num_heads
								UpperCAmelCase_		:      List[str]       	=					num_experts
								UpperCAmelCase_		:      List[str]       	=					expert_capacity
								UpperCAmelCase_		:      Optional[int]       	=					dropout_rate
								UpperCAmelCase_		:      List[Any]       	=					layer_norm_epsilon
								UpperCAmelCase_		:      Any       	=					router_bias
								UpperCAmelCase_		:      Optional[Any]       	=					router_jitter_noise
								UpperCAmelCase_		:      str       	=					router_dtype
								UpperCAmelCase_		:      List[str]       	=					router_ignore_padding_tokens
								UpperCAmelCase_		:      Optional[int]       	=					output_hidden_states
								UpperCAmelCase_		:      Union[str, Any]       	=					output_attentions
								UpperCAmelCase_		:      Optional[int]       	=					initializer_factor
								UpperCAmelCase_		:      Optional[int]       	=					output_router_logits
								UpperCAmelCase_		:      List[str]       	=					use_cache

								super().__init__(
								    separator_token_id=_snake_case    ,			pad_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case    ,			)






