






"""simple docstring"""

import argparse

import tensorflow as tf
import torch

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


logging.set_verbosity_info()






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						def get_masked_lm_array(__lowerCamelCase:str   ):
												__magic_name__            =     f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__magic_name__            =     tf.train.load_variable(__lowerCamelCase ,						__lowerCamelCase   )

												if "kernel" in name:
																		__magic_name__            =     array.transpose()

												return torch.from_numpy(__lowerCamelCase   )

						def get_encoder_array(__lowerCamelCase:str   ):
												__magic_name__            =     f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__magic_name__            =     tf.train.load_variable(__lowerCamelCase ,						__lowerCamelCase   )

												if "kernel" in name:
																		__magic_name__            =     array.transpose()

												return torch.from_numpy(__lowerCamelCase   )

						def get_encoder_layer_array(__lowerCamelCase:int ,						__lowerCamelCase:str   ):
												__magic_name__            =     f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__magic_name__            =     tf.train.load_variable(__lowerCamelCase ,						__lowerCamelCase   )

												if "kernel" in name:
																		__magic_name__            =     array.transpose()

												return torch.from_numpy(__lowerCamelCase   )

						def get_encoder_attention_layer_array(__lowerCamelCase:int ,						__lowerCamelCase:str ,						__lowerCamelCase:Any   ):
												__magic_name__            =     f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__magic_name__            =     tf.train.load_variable(__lowerCamelCase ,						__lowerCamelCase   )
												__magic_name__            =     array.reshape(__lowerCamelCase   )

												if "kernel" in name:
																		__magic_name__            =     array.transpose()

												return torch.from_numpy(__lowerCamelCase   )

						print(f'''Loading model based on config from {config_path}...'''   )
						__magic_name__            =     BertConfig.from_json_file(__lowerCamelCase   )
						__magic_name__            =     BertForMaskedLM(__lowerCamelCase   )

						# Layers
						for layer_index in range(0 ,						config.num_hidden_layers   ):
												__magic_name__            =     model.bert.encoder.layer[layer_index]

												# Self-attention
												__magic_name__            =     layer.attention.self

												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_query_dense/kernel" ,						self_attn.query.weight.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_query_dense/bias" ,						self_attn.query.bias.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_key_dense/kernel" ,						self_attn.key.weight.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_key_dense/bias" ,						self_attn.key.bias.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_value_dense/kernel" ,						self_attn.value.weight.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_value_dense/bias" ,						self_attn.value.bias.data.shape   )

												# Self-attention Output
												__magic_name__            =     layer.attention.output

												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_output_dense/kernel" ,						self_output.dense.weight.data.shape   )
												__magic_name__            =     get_encoder_attention_layer_array(
												    __lowerCamelCase ,						"_output_dense/bias" ,						self_output.dense.bias.data.shape   )

												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_attention_layer_norm/gamma"   )
												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_attention_layer_norm/beta"   )

												# Intermediate
												__magic_name__            =     layer.intermediate

												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_intermediate_dense/kernel"   )
												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_intermediate_dense/bias"   )

												# Output
												__magic_name__            =     layer.output

												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_output_dense/kernel"   )
												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_output_dense/bias"   )

												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_output_layer_norm/gamma"   )
												__magic_name__            =     get_encoder_layer_array(__lowerCamelCase ,						"_output_layer_norm/beta"   )

						# Embeddings
						__magic_name__            =     get_encoder_array("_position_embedding_layer/embeddings"   )
						__magic_name__            =     get_encoder_array("_type_embedding_layer/embeddings"   )
						__magic_name__            =     get_encoder_array("_embedding_norm_layer/gamma"   )
						__magic_name__            =     get_encoder_array("_embedding_norm_layer/beta"   )

						# LM Head
						__magic_name__            =     model.cls.predictions.transform

						__magic_name__            =     get_masked_lm_array("dense/kernel"   )
						__magic_name__            =     get_masked_lm_array("dense/bias"   )

						__magic_name__            =     get_masked_lm_array("layer_norm/gamma"   )
						__magic_name__            =     get_masked_lm_array("layer_norm/beta"   )

						__magic_name__            =     get_masked_lm_array("embedding_table"   )

						# Pooling
						__magic_name__            =     BertPooler(config=__lowerCamelCase   )
						__magic_name__            =     get_encoder_array("_pooler_layer/kernel"   )
						__magic_name__            =     get_encoder_array("_pooler_layer/bias"   )

						# Export final model
						model.save_pretrained(__lowerCamelCase   )

						# Integration test - should load without any errors ;)
						__magic_name__            =     BertForMaskedLM.from_pretrained(__lowerCamelCase   )
						print(new_model.eval()   )

						print("Model conversion was done sucessfully!"   )


if __name__ == "__main__":
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument(
			    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
			)
			parser.add_argument(
			    '''--bert_config_file''',
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''',
			    type=str,
			    required=True,
			    help='''Path to the output PyTorch model.''',
			)
			lowercase    			=       parser.parse_args()
			convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""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


lowercase    			=       get_tests_dir('''fixtures/test_sentencepiece.model''')

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

lowercase    			=       250004
lowercase    			=       250020

@require_sentencepiece
@require_tokenizers
class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				MBartaaTokenizer
				UpperCAmelCase__        =				MBartaaTokenizerFast
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				True







				def     _snake_case					(							self							:						Any	)				->					Dict:
										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__            =     MBartaaTokenizer(__lowerCamelCase						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=__lowerCamelCase	)
										tokenizer.save_pretrained(self.tmpdirname	)







				def     _snake_case					(							self							:						Dict	)				->					List[Any]:
										__magic_name__            =     "<s>"
										__magic_name__            =     0

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







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										__magic_name__            =     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(__lowerCamelCase	)						,    1_0_5_4	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										self.assertEqual(self.get_tokenizer().vocab_size						,    1_0_5_4	)







				def     _snake_case					(							self							:						str	)				->					Optional[Any]:
										__magic_name__            =     MBartaaTokenizer(__lowerCamelCase						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=__lowerCamelCase	)

										__magic_name__            =     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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]						,    )

										__magic_name__            =     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", "."]						,    )
										__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										    ]						,    )

										__magic_name__            =     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>", "."]						,    )







				@slow
				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										# fmt: off
										__magic_name__            =     {"input_ids": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=__lowerCamelCase						,    model_name="facebook/mbart-large-50"						,    revision="d3913889c59cd5c9e456b269c376325eabad57e2"						,    )







				def     _snake_case					(							self							:						int	)				->					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

										__magic_name__            =     (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})'''	):
																						__magic_name__            =     self.rust_tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)
																						__magic_name__            =     self.tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)

																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase	)
																						__magic_name__            =     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	)	)
																						__magic_name__            =     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
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     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
																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase						,    legacy_format=__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.save_pretrained(__lowerCamelCase	)

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

																						# Checks everything loads correctly in the same way
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     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
																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase						,    legacy_format=__lowerCamelCase	)
																						__magic_name__            =     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
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     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 						A_							(      unittest.TestCase  ):
				UpperCAmelCase__        =				'''facebook/mbart-large-50-one-to-many-mmt'''
				UpperCAmelCase__        =				[
				    ''' 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.''',
				]
				UpperCAmelCase__        =				[
				    '''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.''',
				]
				UpperCAmelCase__        =				[EN_CODE, 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]







				@classmethod
				def     _snake_case					(							cls							:						Any	)				->					Dict:
										__magic_name__            =     MBartaaTokenizer.from_pretrained(
										    cls.checkpoint_name						,    src_lang="en_XX"						,    tgt_lang="ro_RO"	)
										__magic_name__            =     1
										return cls







				def     _snake_case					(							self							:						Any	)				->					Dict:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]						,    2_5_0_0_0_1	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]						,    2_5_0_0_0_4	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]						,    2_5_0_0_2_0	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]						,    2_5_0_0_3_8	)







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







				def     _snake_case					(							self							:						Any	)				->					List[str]:
										self.assertIn(__lowerCamelCase						,    self.tokenizer.all_special_ids	)
										__magic_name__            =     [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
										__magic_name__            =     self.tokenizer.decode(__lowerCamelCase						,    skip_special_tokens=__lowerCamelCase	)
										__magic_name__            =     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							:						int	)				->					List[str]:
										__magic_name__            =     ["this is gunna be a long sentence " * 2_0]
										assert isinstance(src_text[0]						,    __lowerCamelCase	)
										__magic_name__            =     1_0
										__magic_name__            =     self.tokenizer(__lowerCamelCase						,    max_length=__lowerCamelCase						,    truncation=__lowerCamelCase	).input_ids[0]
										self.assertEqual(ids[0]						,    __lowerCamelCase	)
										self.assertEqual(ids[-1]						,    2	)
										self.assertEqual(len(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Tuple:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)						,    [2_5_0_0_5_3, 2_5_0_0_0_1]	)







				def     _snake_case					(							self							:						Any	)				->					str:
										__magic_name__            =     tempfile.mkdtemp()
										__magic_name__            =     self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__lowerCamelCase	)
										__magic_name__            =     MBartaaTokenizer.from_pretrained(__lowerCamelCase	)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,    __lowerCamelCase	)







				@require_torch
				def     _snake_case					(							self							:						int	)				->					Dict:
										__magic_name__            =     self.tokenizer(self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    return_tensors="pt"	)
										__magic_name__            =     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							:						Dict	)				->					Tuple:
										__magic_name__            =     self.tokenizer(
										    self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=len(self.expected_src_tokens	)						,    return_tensors="pt"						,    )
										__magic_name__            =     shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id	)

										self.assertIsInstance(__lowerCamelCase						,    __lowerCamelCase	)

										self.assertEqual((2, 1_4)						,    batch.input_ids.shape	)
										self.assertEqual((2, 1_4)						,    batch.attention_mask.shape	)
										__magic_name__            =     batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens						,    __lowerCamelCase	)
										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							:						Optional[int]	)				->					Dict:
										__magic_name__            =     self.tokenizer(self.src_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=3						,    return_tensors="pt"	)
										__magic_name__            =     self.tokenizer(
										    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=1_0						,    return_tensors="pt"	)
										__magic_name__            =     targets["input_ids"]
										__magic_name__            =     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]						,    1_0	)







				@require_torch
				def     _snake_case					(							self							:						Optional[int]	)				->					Tuple:
										__magic_name__            =     self.tokenizer._build_translation_inputs(
										    "A test"						,    return_tensors="pt"						,    src_lang="en_XX"						,    tgt_lang="ar_AR"	)

										self.assertEqual(
										    nested_simplify(__lowerCamelCase	)						,    {
										        # en_XX, A, test, EOS
										        "input_ids": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 2_5_0_0_0_1,
										    }						,    )



