



"""simple docstring"""






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
		raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
		raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
_lowercase				:				str   		=		logging.get_logger(__name__)

_lowercase				:				Optional[Any]   		=		"The Nymphenburg Palace is a beautiful palace in Munich!"






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str			):
			"""simple docstring"""
			lowerCamelCase__	:	List[Any]				={
			    '''attention_cell''': '''multi_head''',
			    '''num_layers''': 4,
			    '''units''': 1024,
			    '''hidden_size''': 768,
			    '''max_length''': 512,
			    '''num_heads''': 8,
			    '''scaled''': True,
			    '''dropout''': 0.1,
			    '''use_residual''': True,
			    '''embed_size''': 1024,
			    '''embed_dropout''': 0.1,
			    '''word_embed''': None,
			    '''layer_norm_eps''': 1e-5,
			    '''token_type_vocab_size''': 2,
			}

			lowerCamelCase__	:	List[str]				=bort_4_8_768_1024_hparams

			# Let's construct the original Bort model here
			# Taken from official BERT implementation, see:
			# https://github.com/alexa/bort/blob/master/bort/bort.py
			lowerCamelCase__	:	int				=BERTEncoder(
			    attention_cell=predefined_args['''attention_cell''']							,	num_layers=predefined_args['''num_layers''']							,	units=predefined_args['''units''']							,	hidden_size=predefined_args['''hidden_size''']							,	max_length=predefined_args['''max_length''']							,	num_heads=predefined_args['''num_heads''']							,	scaled=predefined_args['''scaled''']							,	dropout=predefined_args['''dropout''']							,	output_attention=__lowerCamelCase							,	output_all_encodings=__lowerCamelCase							,	use_residual=predefined_args['''use_residual''']							,	activation=predefined_args.get('''activation'''							,	'''gelu'''			)							,	layer_norm_eps=predefined_args.get('''layer_norm_eps'''							,	__lowerCamelCase			)							,	)

			# Vocab information needs to be fetched first
			# It's the same as RoBERTa, so RobertaTokenizer can be used later
			lowerCamelCase__	:	Optional[Any]				='''openwebtext_ccnews_stories_books_cased'''

			# Specify download folder to Gluonnlp's vocab
			lowerCamelCase__	:	Union[str, Any]				=os.path.join(get_home_dir()							,	'''models'''			)
			lowerCamelCase__	:	Any				=_load_vocab(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	cls=__lowerCamelCase			)

			lowerCamelCase__	:	Any				=nlp.model.BERTModel(
			    __lowerCamelCase							,	len(__lowerCamelCase			)							,	units=predefined_args['''units''']							,	embed_size=predefined_args['''embed_size''']							,	embed_dropout=predefined_args['''embed_dropout''']							,	word_embed=predefined_args['''word_embed''']							,	use_pooler=__lowerCamelCase							,	use_token_type_embed=__lowerCamelCase							,	token_type_vocab_size=predefined_args['''token_type_vocab_size''']							,	use_classifier=__lowerCamelCase							,	use_decoder=__lowerCamelCase							,	)

			original_bort.load_parameters(__lowerCamelCase							,	cast_dtype=__lowerCamelCase							,	ignore_extra=__lowerCamelCase			)
			lowerCamelCase__	:	Optional[int]				=original_bort._collect_params_with_prefix()

			# Build our config 🤗
			lowerCamelCase__	:	Union[str, Any]				={
			    '''architectures''': ['''BertForMaskedLM'''],
			    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
			    '''hidden_act''': '''gelu''',
			    '''hidden_dropout_prob''': predefined_args['''dropout'''],
			    '''hidden_size''': predefined_args['''embed_size'''],
			    '''initializer_range''': 0.02,
			    '''intermediate_size''': predefined_args['''hidden_size'''],
			    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
			    '''max_position_embeddings''': predefined_args['''max_length'''],
			    '''model_type''': '''bort''',
			    '''num_attention_heads''': predefined_args['''num_heads'''],
			    '''num_hidden_layers''': predefined_args['''num_layers'''],
			    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
			    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
			    '''vocab_size''': len(__lowerCamelCase			),
			}

			lowerCamelCase__	:	int				=BertConfig.from_dict(__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=BertForMaskedLM(__lowerCamelCase			)
			hf_bort_model.eval()

			# Parameter mapping table (Gluonnlp to Transformers)
			# * denotes layer index
			#
			# | Gluon Parameter                                                | Transformers Parameter
			# | -------------------------------------------------------------- | ----------------------
			# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
			# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
			# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
			# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
			# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
			# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
			# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

			# Helper function to convert MXNET Arrays to PyTorch
			def to_torch(__lowerCamelCase       :					Dict			) -> nn.Parameter:
						return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()			)			)

			# Check param shapes and map new HF param back
			def check_and_map_params(__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Optional[int]			):
						lowerCamelCase__	:	Union[str, Any]				=hf_param.shape

						lowerCamelCase__	:	Optional[int]				=to_torch(params[gluon_param]			)
						lowerCamelCase__	:	Optional[int]				=gluon_param.shape

						assert (
						    shape_hf == shape_gluon
						), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

						return gluon_param

			lowerCamelCase__	:	Union[str, Any]				=check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight							,	'''word_embed.0.weight'''			)
			lowerCamelCase__	:	Tuple				=check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight							,	'''encoder.position_weight'''			)
			lowerCamelCase__	:	Dict				=check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias							,	'''encoder.layer_norm.beta'''			)
			lowerCamelCase__	:	Union[str, Any]				=check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.weight							,	'''encoder.layer_norm.gamma'''			)

			# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
			lowerCamelCase__	:	Union[str, Any]				=torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

			for i in range(hf_bort_config.num_hidden_layers			):
						lowerCamelCase__	:	BertLayer				=hf_bort_model.bert.encoder.layer[i]

						# self attention
						lowerCamelCase__	:	BertSelfAttention				=layer.attention.self

						lowerCamelCase__	:	Optional[Any]				=check_and_map_params(
						    self_attn.key.bias.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''			)

						lowerCamelCase__	:	Optional[int]				=check_and_map_params(
						    self_attn.key.weight.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''			)
						lowerCamelCase__	:	List[Any]				=check_and_map_params(
						    self_attn.query.bias.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''			)
						lowerCamelCase__	:	Optional[Any]				=check_and_map_params(
						    self_attn.query.weight.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''			)
						lowerCamelCase__	:	str				=check_and_map_params(
						    self_attn.value.bias.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''			)
						lowerCamelCase__	:	List[str]				=check_and_map_params(
						    self_attn.value.weight.data							,	f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''			)

						# self attention output
						lowerCamelCase__	:	BertSelfOutput				=layer.attention.output

						lowerCamelCase__	:	Dict				=check_and_map_params(
						    self_output.dense.bias							,	f'''encoder.transformer_cells.{i}.proj.bias'''			)
						lowerCamelCase__	:	Any				=check_and_map_params(
						    self_output.dense.weight							,	f'''encoder.transformer_cells.{i}.proj.weight'''			)
						lowerCamelCase__	:	str				=check_and_map_params(
						    self_output.LayerNorm.bias							,	f'''encoder.transformer_cells.{i}.layer_norm.beta'''			)
						lowerCamelCase__	:	Tuple				=check_and_map_params(
						    self_output.LayerNorm.weight							,	f'''encoder.transformer_cells.{i}.layer_norm.gamma'''			)

						# intermediate
						lowerCamelCase__	:	BertIntermediate				=layer.intermediate

						lowerCamelCase__	:	Union[str, Any]				=check_and_map_params(
						    intermediate.dense.bias							,	f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''			)
						lowerCamelCase__	:	Any				=check_and_map_params(
						    intermediate.dense.weight							,	f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''			)

						# output
						lowerCamelCase__	:	BertOutput				=layer.output

						lowerCamelCase__	:	int				=check_and_map_params(
						    bert_output.dense.bias							,	f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''			)
						lowerCamelCase__	:	Any				=check_and_map_params(
						    bert_output.dense.weight							,	f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''			)
						lowerCamelCase__	:	Optional[Any]				=check_and_map_params(
						    bert_output.LayerNorm.bias							,	f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''			)
						lowerCamelCase__	:	Union[str, Any]				=check_and_map_params(
						    bert_output.LayerNorm.weight							,	f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''			)

			# Save space and energy 🎄
			hf_bort_model.half()

			# Compare output of both models
			lowerCamelCase__	:	Dict				=RobertaTokenizer.from_pretrained('''roberta-base'''			)

			lowerCamelCase__	:	Tuple				=tokenizer.encode_plus(__lowerCamelCase			)['''input_ids''']

			# Get gluon output
			lowerCamelCase__	:	Optional[Any]				=mx.nd.array([input_ids]			)
			lowerCamelCase__	:	Optional[Any]				=original_bort(inputs=__lowerCamelCase							,	token_types=[]			)

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	int				=BertModel.from_pretrained(__lowerCamelCase			)
			hf_bort_model.eval()

			lowerCamelCase__	:	Union[str, Any]				=tokenizer.encode_plus(__lowerCamelCase							,	return_tensors='''pt'''			)
			lowerCamelCase__	:	Any				=hf_bort_model(**__lowerCamelCase			)[0]

			lowerCamelCase__	:	Dict				=output_gluon[0].asnumpy()
			lowerCamelCase__	:	Union[str, Any]				=output_hf[0].detach().numpy()

			lowerCamelCase__	:	List[Any]				=np.max(np.abs(hf_layer - gluon_layer			)			).item()
			lowerCamelCase__	:	List[Any]				=np.allclose(__lowerCamelCase							,	__lowerCamelCase							,	atol=1e-3			)

			if success:
						print('''✔️ Both model do output the same tensors'''			)
			else:
						print('''❌ Both model do **NOT** output the same tensors'''			)
						print('''Absolute difference is:'''							,	__lowerCamelCase			)


if __name__ == "__main__":
		_lowercase				:				List[str]   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase				:				List[str]   		=		parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase				:				List[str]   		=		get_tests_dir("fixtures/test_sentencepiece.model")


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

_lowercase				:				List[str]   		=		2_5_0_0_0_4
_lowercase				:				Optional[Any]   		=		2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							MBartTokenizer
					_a         =							MBartTokenizerFast
					_a         =							True
					_a         =							True



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__	:	Union[str, Any]				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )
								tokenizer.save_pretrained(self.tmpdirname  )



					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=tokenizer.tokenize('''This is a test'''  )
								self.assertListEqual(lowerCamelCase,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

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

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

								lowerCamelCase__	:	str				=tokenizer.convert_ids_to_tokens(lowerCamelCase  )
								self.assertListEqual(
								    lowerCamelCase,						[
								        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 	snake_case ( self							:						Tuple  )->      List[Any]:
								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

								lowerCamelCase__	:	int				=(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														lowerCamelCase__	:	Union[str, Any]				=self.rust_tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )
														lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )

														lowerCamelCase__	:	List[str]				=tempfile.mkdtemp()

														lowerCamelCase__	:	Union[str, Any]				=tokenizer_r.save_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# 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  )  )
														lowerCamelCase__	:	List[str]				=tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Any				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.from_pretrained(lowerCamelCase  )

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

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__	:	Dict				=tempfile.mkdtemp()

														lowerCamelCase__	:	List[str]				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Tuple				=tokenizer_p.save_pretrained(lowerCamelCase  )

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

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

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

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=False
														lowerCamelCase__	:	Optional[int]				=tempfile.mkdtemp()

														lowerCamelCase__	:	int				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# 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
														lowerCamelCase__	:	Dict				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	int				=tokenizer_p.from_pretrained(lowerCamelCase  )

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

														shutil.rmtree(lowerCamelCase  )





@require_torch
@require_sentencepiece
@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							'facebook/mbart-large-en-ro'
					_a         =							[
					    ' 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.',
					]
					_a         =							[
					    '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.',
					]
					_a         =							[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



					@classmethod
					def 	snake_case ( cls							:						List[Any]  )->      Optional[int]:
								lowerCamelCase__	:	MBartTokenizer				=MBartTokenizer.from_pretrained(
								    cls.checkpoint_name,						src_lang='''en_XX''',						tgt_lang='''ro_RO'''  )
								lowerCamelCase__	:	Optional[int]				=1
								return cls



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								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  )



					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								self.assertIn(lowerCamelCase,						self.tokenizer.all_special_ids  )
								lowerCamelCase__	:	Optional[int]				=[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								lowerCamelCase__	:	Any				=self.tokenizer.decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase  )
								lowerCamelCase__	:	str				=self.tokenizer.decode(generated_ids[1:],						skip_special_tokens=lowerCamelCase  )
								self.assertEqual(lowerCamelCase,						lowerCamelCase  )
								self.assertNotIn(self.tokenizer.eos_token,						lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Optional[int]				=['''this is gunna be a long sentence ''' * 20]
								assert isinstance(src_text[0],						lowerCamelCase  )
								lowerCamelCase__	:	Dict				=10
								lowerCamelCase__	:	Optional[int]				=self.tokenizer(lowerCamelCase,						max_length=lowerCamelCase,						truncation=lowerCamelCase  ).input_ids[0]
								self.assertEqual(ids[-2],						2  )
								self.assertEqual(ids[-1],						lowerCamelCase  )
								self.assertEqual(len(lowerCamelCase  ),						lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      Any:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']  ),						[25_0026, 25_0001]  )



					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	int				=tempfile.mkdtemp()
								lowerCamelCase__	:	Optional[int]				=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=MBartTokenizer.from_pretrained(lowerCamelCase  )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,						lowerCamelCase  )



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=self.tokenizer(self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Dict				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

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



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__	:	str				=self.tokenizer(
								    self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=len(self.expected_src_tokens  ),						return_tensors='''pt''',						)

								lowerCamelCase__	:	List[Any]				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								self.assertIsInstance(lowerCamelCase,						lowerCamelCase  )

								self.assertEqual((2, 14),						batch.input_ids.shape  )
								self.assertEqual((2, 14),						batch.attention_mask.shape  )
								lowerCamelCase__	:	Any				=batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )
								self.assertEqual(2,						batch.decoder_input_ids[0, -1]  )  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens,						[]  )
								self.assertEqual(self.tokenizer.suffix_tokens,						[self.tokenizer.eos_token_id, EN_CODE]  )



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								lowerCamelCase__	:	Any				=self.tokenizer(self.src_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=3,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Tuple				=self.tokenizer(
								    text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=10,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Union[str, Any]				=targets['''input_ids''']
								lowerCamelCase__	:	List[Any]				=shift_tokens_right(lowerCamelCase,						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							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	str				=self.tokenizer._build_translation_inputs(
								    '''A test''',						return_tensors='''pt''',						src_lang='''en_XX''',						tgt_lang='''ar_AR'''  )

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




