





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
		raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
		raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
lowercase_		:				Any				=						logging.get_logger(__name__)

lowercase_		:				List[Any]				=						'Hello, World!'
lowercase_		:				str				=						'en_XX'

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   Path('data_bin' )
				_snake_case :			int  						=   FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(__UpperCamelCase ).parent )	,			checkpoint_file=Path(__UpperCamelCase ).name	,			_name='xmod_base'	,			arch='xmod_base'	,			task='multilingual_masked_lm'	,			data_name_or_path=str(__UpperCamelCase )	,			bpe='sentencepiece'	,			sentencepiece_model=str(Path(__UpperCamelCase ).parent / 'sentencepiece.bpe.model' )	,			src_dict=str(data_dir / 'dict.txt' )	,			)
				xmod.eval()  # disable dropout
				print(__UpperCamelCase )

				_snake_case :			List[Any]  						=   xmod.model.encoder.sentence_encoder
				_snake_case :			List[Any]  						=   XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings	,			hidden_size=xmod.cfg.model.encoder_embed_dim	,			num_hidden_layers=xmod.cfg.model.encoder_layers	,			num_attention_heads=xmod.cfg.model.encoder_attention_heads	,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim	,			max_position_embeddings=5_14	,			type_vocab_size=1	,			layer_norm_eps=1E-5	,			pre_norm=xmod.cfg.model.encoder_normalize_before	,			adapter_reduction_factor=getattr(xmod.cfg.model	,			'bottleneck'	,			2 )	,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm	,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm	,			ln_before_adapter=xmod.cfg.model.ln_before_adapter	,			languages=xmod.cfg.model.languages	,			)
				if classification_head:
								_snake_case :			Optional[Any]  						=   xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('Our X-MOD config:'	,			__UpperCamelCase )

				_snake_case :			Optional[int]  						=   XmodForSequenceClassification(__UpperCamelCase ) if classification_head else XmodForMaskedLM(__UpperCamelCase )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				_snake_case :			List[Any]  						=   xmod_sent_encoder.embed_tokens.weight
				_snake_case :			int  						=   xmod_sent_encoder.embed_positions.weight
				_snake_case :			int  						=   torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c xmod doesn't use them.

				_snake_case :			Optional[int]  						=   xmod_sent_encoder.layernorm_embedding.weight
				_snake_case :			Optional[int]  						=   xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers ):
								# Encoder: start of layer
								_snake_case :			Optional[int]  						=   model.roberta.encoder.layer[i]
								_snake_case :			int  						=   xmod_sent_encoder.layers[i]

								# self attention
								_snake_case :			Any  						=   layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size) )
								):
												raise AssertionError('Dimensions of self-attention weights do not match.' )

								_snake_case :			Dict  						=   xmod_layer.self_attn.q_proj.weight
								_snake_case :			Optional[Any]  						=   xmod_layer.self_attn.q_proj.bias
								_snake_case :			Optional[Any]  						=   xmod_layer.self_attn.k_proj.weight
								_snake_case :			List[str]  						=   xmod_layer.self_attn.k_proj.bias
								_snake_case :			Any  						=   xmod_layer.self_attn.v_proj.weight
								_snake_case :			int  						=   xmod_layer.self_attn.v_proj.bias

								# self-attention output
								_snake_case :			Optional[Any]  						=   layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('Dimensions of self-attention output weights do not match.' )
								_snake_case :			Any  						=   xmod_layer.self_attn.out_proj.weight
								_snake_case :			List[Any]  						=   xmod_layer.self_attn.out_proj.bias
								_snake_case :			Tuple  						=   xmod_layer.self_attn_layer_norm.weight
								_snake_case :			Optional[Any]  						=   xmod_layer.self_attn_layer_norm.bias

								# intermediate
								_snake_case :			int  						=   layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('Dimensions of intermediate weights do not match.' )
								_snake_case :			List[str]  						=   xmod_layer.fca.weight
								_snake_case :			Any  						=   xmod_layer.fca.bias

								# output
								_snake_case :			List[Any]  						=   layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('Dimensions of feed-forward weights do not match.' )
								_snake_case :			Any  						=   xmod_layer.fca.weight
								_snake_case :			int  						=   xmod_layer.fca.bias
								_snake_case :			List[Any]  						=   xmod_layer.final_layer_norm.weight
								_snake_case :			List[str]  						=   xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												_snake_case :			Dict  						=   xmod_layer.adapter_layer_norm.weight
												_snake_case :			List[str]  						=   xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
												raise AssertionError('Lists of language adapters do not match.' )
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												_snake_case :			List[str]  						=   bert_output.adapter_modules[lang_code]
												_snake_case :			Dict  						=   xmod_layer.adapter_modules[lang_code]
												_snake_case :			Dict  						=   from_adapter.fca.weight
												_snake_case :			List[str]  						=   from_adapter.fca.bias
												_snake_case :			Dict  						=   from_adapter.fca.weight
												_snake_case :			Dict  						=   from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								_snake_case :			Dict  						=   xmod_sent_encoder.layer_norm.weight
								_snake_case :			int  						=   xmod_sent_encoder.layer_norm.bias

				if classification_head:
								_snake_case :			Any  						=   xmod.model.classification_heads['''mnli'''].dense.weight
								_snake_case :			int  						=   xmod.model.classification_heads['''mnli'''].dense.bias
								_snake_case :			Union[str, Any]  						=   xmod.model.classification_heads['''mnli'''].out_proj.weight
								_snake_case :			Optional[Any]  						=   xmod.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								_snake_case :			List[str]  						=   xmod.model.encoder.lm_head.dense.weight
								_snake_case :			int  						=   xmod.model.encoder.lm_head.dense.bias
								_snake_case :			Optional[int]  						=   xmod.model.encoder.lm_head.layer_norm.weight
								_snake_case :			List[str]  						=   xmod.model.encoder.lm_head.layer_norm.bias
								_snake_case :			str  						=   xmod.model.encoder.lm_head.weight
								_snake_case :			Any  						=   xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				_snake_case :			List[Any]  						=   xmod.encode(__UpperCamelCase ).unsqueeze(0 )  # batch of size 1
				model.roberta.set_default_language(__UpperCamelCase )

				_snake_case :			str  						=   model(__UpperCamelCase )[0]
				if classification_head:
								_snake_case :			Dict  						=   xmod.model.classification_heads['''mnli'''](xmod.extract_features(__UpperCamelCase ) )
				else:
								_snake_case :			Optional[Any]  						=   xmod.model(__UpperCamelCase	,			lang_id=[SAMPLE_LANGUAGE] )[0]
				print(our_output.shape	,			their_output.shape )
				_snake_case :			List[str]  						=   torch.max(torch.abs(our_output - their_output ) ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
				_snake_case :			int  						=   torch.allclose(__UpperCamelCase	,			__UpperCamelCase	,			atol=1E-3 )
				print('Do both models output the same tensors?'	,			'🔥' if success else '💩' )
				if not success:
								raise Exception('Something went wRoNg' )

				Path(__UpperCamelCase ).mkdir(parents=__UpperCamelCase	,			exist_ok=__UpperCamelCase )
				print(F'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		lowercase_		:				Dict				=						parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)













import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase_		:				Dict				=						'''bert-base-cased'''
lowercase_		:				Any				=						'''google/pegasus-xsum'''
lowercase_		:				str				=						[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase_		:				Tuple				=						['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase_		:				Any				=						'''patrickvonplaten/t5-tiny-random'''
lowercase_		:				List[Any]				=						'''sshleifer/bart-tiny-random'''
lowercase_		:				Dict				=						'''sshleifer/tiny-mbart'''
lowercase_		:				str				=						'''sshleifer/tiny-marian-en-de'''

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			str  						=   '\n'.join(__lowerCAmelCase )
				Path(__lowerCAmelCase ).open('w' ).writelines(__lowerCAmelCase )

def       A__(		__lowerCAmelCase ):
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.source''' )	,			__lowerCAmelCase )
								_dump_articles(os.path.join(__lowerCAmelCase	,			F'''{split}.target''' )	,			__lowerCAmelCase )
				return tmp_dir


class 			lowercase				(							a_     ):



						"""simple docstring"""

						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			Dict  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Optional[int]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Any  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Dict  						=   4
										_snake_case :			Any  						=   8
										assert max_len_target > max_src_len  # Will be truncated
										assert max_len_source > max_src_len  # Will be truncated
										_snake_case						,			_snake_case :			Optional[Any]  						=   'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
										_snake_case :			int  						=   SeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=lowerCamelCase_ ,				max_target_length=lowerCamelCase_ ,				src_lang=lowerCamelCase_ ,				tgt_lang=lowerCamelCase_ ,				)
										_snake_case :			List[str]  						=   DataLoader(lowerCamelCase_ ,				batch_size=2 ,				collate_fn=train_dataset.collate_fn					)
										for batch in dataloader:
														assert isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_src_len
														# show that targets are the same len
														assert batch["labels"].shape[1] == max_tgt_len
														if tok_name != MBART_TINY:
																		continue
														# check language codes in correct place
														_snake_case :			List[Any]  						=   shift_tokens_right(batch['labels'] ,				tokenizer.pad_token_id					)
														assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
														assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
														assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

														break  # No need to test every batch







						@parameterized.expand([BART_TINY, BERT_BASE_CASED]					)
						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[Any]  						=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case :			Dict  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in ARTICLES					)
										_snake_case :			Union[str, Any]  						=   max(len(tokenizer.encode(lowerCamelCase_					)					) for a in SUMMARIES					)
										_snake_case :			Union[str, Any]  						=   4
										_snake_case :			Optional[int]  						=   LegacySeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=20 ,				max_target_length=lowerCamelCase_ ,				)
										_snake_case :			Dict  						=   DataLoader(lowerCamelCase_ ,				batch_size=2 ,				collate_fn=train_dataset.collate_fn					)
										for batch in dataloader:
														assert batch["attention_mask"].shape == batch["input_ids"].shape
														# show that articles were trimmed.
														assert batch["input_ids"].shape[1] == max_len_source
														assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
														# show that targets were truncated
														assert batch["labels"].shape[1] == trunc_target  # Truncated
														assert max_len_target > trunc_target  # Truncated
														break  # No need to test every batch







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			int  						=   AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

										_snake_case :			List[str]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										_snake_case :			Any  						=   tmp_dir.joinpath('train.source'					).open().readlines()
										_snake_case :			Optional[Any]  						=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
										pack_data_dir(lowerCamelCase_ ,				lowerCamelCase_ ,				1_28 ,				lowerCamelCase_					)
										_snake_case :			Tuple  						=   {x.name for x in tmp_dir.iterdir()}
										_snake_case :			Dict  						=   {x.name for x in save_dir.iterdir()}
										_snake_case :			str  						=   save_dir.joinpath('train.source'					).open().readlines()
										# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
										# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
										assert len(lowerCamelCase_					) < len(lowerCamelCase_					)
										assert len(lowerCamelCase_					) == 1
										assert len(packed_examples[0]					) == sum(len(lowerCamelCase_					) for x in orig_examples					)
										assert orig_paths == new_paths







						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,				reason='This test requires fairseq'					)
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										if not FAIRSEQ_AVAILABLE:
														return
										_snake_case						,			_snake_case						,			_snake_case :			int  						=   self._get_dataset(max_len=64					)
										_snake_case :			List[str]  						=   64
										_snake_case :			str  						=   ds.make_dynamic_sampler(lowerCamelCase_ ,				required_batch_size_multiple=lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   [len(lowerCamelCase_					) for x in batch_sampler]
										assert len(set(lowerCamelCase_					)					) > 1  # it's not dynamic batch size if every batch is the same length
										assert sum(lowerCamelCase_					) == len(lowerCamelCase_					)  # no dropped or added examples
										_snake_case :			Union[str, Any]  						=   DataLoader(lowerCamelCase_ ,				batch_sampler=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2					)
										_snake_case :			List[Any]  						=   []
										_snake_case :			List[Any]  						=   []
										for batch in data_loader:
														_snake_case :			Any  						=   batch['input_ids'].shape
														_snake_case :			str  						=   src_shape[0]
														assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
														_snake_case :			int  						=   np.product(batch['input_ids'].shape					)
														num_src_per_batch.append(lowerCamelCase_					)
														if num_src_tokens > (max_tokens * 1.1):
																		failures.append(lowerCamelCase_					)
										assert num_src_per_batch[0] == max(lowerCamelCase_					)
										if failures:
														raise AssertionError(f'''too many tokens in {len(lowerCamelCase_					)} batches'''					)







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			str  						=   self._get_dataset(max_len=5_12					)
										_snake_case :			Optional[Any]  						=   2
										_snake_case :			Dict  						=   ds.make_sortish_sampler(lowerCamelCase_ ,				shuffle=lowerCamelCase_					)

										_snake_case :			int  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2					)
										_snake_case :			str  						=   DataLoader(lowerCamelCase_ ,				batch_size=lowerCamelCase_ ,				collate_fn=ds.collate_fn ,				num_workers=2 ,				sampler=lowerCamelCase_					)

										_snake_case :			Tuple  						=   tokenizer.pad_token_id

										def count_pad_tokens(lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		Any="input_ids"					):
														return [batch[k].eq(lowerCamelCase_					).sum().item() for batch in data_loader]

										assert sum(count_pad_tokens(lowerCamelCase_ ,				k='labels'					)					) < sum(count_pad_tokens(lowerCamelCase_ ,				k='labels'					)					)
										assert sum(count_pad_tokens(lowerCamelCase_					)					) < sum(count_pad_tokens(lowerCamelCase_					)					)
										assert len(lowerCamelCase_					) == len(lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		Tuple=10_00 ,				lowerCamelCase_			:		Tuple=1_28					):





										'''simple docstring'''







										if os.getenv('USE_REAL_DATA' ,				lowerCamelCase_					):
														_snake_case :			Dict  						=   'examples/seq2seq/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 2 * 64
														if not Path(lowerCamelCase_					).joinpath('train.len'					).exists():
																		save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)
										else:
														_snake_case :			Union[str, Any]  						=   'examples/seq2seq/test_data/wmt_en_ro'
														_snake_case :			List[Any]  						=   max_len * 4
														save_len_file(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_					)
										_snake_case :			str  						=   SeqaSeqDataset(
										    lowerCamelCase_ ,				data_dir=lowerCamelCase_ ,				type_path='train' ,				max_source_length=lowerCamelCase_ ,				max_target_length=lowerCamelCase_ ,				n_obs=lowerCamelCase_ ,				)
										return ds, max_tokens, tokenizer







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case						,			_snake_case						,			_snake_case :			Any  						=   self._get_dataset()
										_snake_case :			List[str]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=0 ,				add_extra_examples=lowerCamelCase_					)					)
										_snake_case :			Optional[Any]  						=   set(DistributedSortishSampler(lowerCamelCase_ ,				2_56 ,				num_replicas=2 ,				rank=1 ,				add_extra_examples=lowerCamelCase_					)					)
										assert idsa.intersection(lowerCamelCase_					) == set()







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,				)
						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   AutoTokenizer.from_pretrained(lowerCamelCase_ ,				use_fast=lowerCamelCase_					)
										if tok_name == MBART_TINY:
														_snake_case :			int  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					) ,				type_path='train' ,				max_source_length=4 ,				max_target_length=8 ,				src_lang='EN' ,				tgt_lang='FR' ,				)
														_snake_case :			Optional[Any]  						=   train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
														_snake_case :			Tuple  						=   SeqaSeqDataset(
														    lowerCamelCase_ ,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					) ,				type_path='train' ,				max_source_length=4 ,				max_target_length=8 ,				)
														_snake_case :			List[Any]  						=   train_dataset.dataset_kwargs
														assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
														assert len(lowerCamelCase_					) == 1 if tok_name == BART_TINY else len(lowerCamelCase_					) == 0






