


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()
A  :     int    					=    logging.get_logger(__name__)

A  :     Tuple    					=    "Hello, World!"
A  :     List[str]    					=    "en_XX"


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Path("data_bin"   )
			__lowerCAmelCase       					=    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   )

			__lowerCAmelCase       					=    xmod.model.encoder.sentence_encoder
			__lowerCAmelCase       					=    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=514					,				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:
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our X-MOD config:"					,				_UpperCamelCase   )

			__lowerCAmelCase       					=    XmodForSequenceClassification(_UpperCamelCase   ) if classification_head else XmodForMaskedLM(_UpperCamelCase   )
			model.eval()

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

			__lowerCAmelCase       					=    xmod_sent_encoder.layernorm_embedding.weight
			__lowerCAmelCase       					=    xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						__lowerCAmelCase       					=    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."   )

						__lowerCAmelCase       					=    xmod_layer.self_attn.q_proj.weight
						__lowerCAmelCase       					=    xmod_layer.self_attn.q_proj.bias
						__lowerCAmelCase       					=    xmod_layer.self_attn.k_proj.weight
						__lowerCAmelCase       					=    xmod_layer.self_attn.k_proj.bias
						__lowerCAmelCase       					=    xmod_layer.self_attn.v_proj.weight
						__lowerCAmelCase       					=    xmod_layer.self_attn.v_proj.bias

						# self-attention output
						__lowerCAmelCase       					=    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."   )
						__lowerCAmelCase       					=    xmod_layer.self_attn.out_proj.weight
						__lowerCAmelCase       					=    xmod_layer.self_attn.out_proj.bias
						__lowerCAmelCase       					=    xmod_layer.self_attn_layer_norm.weight
						__lowerCAmelCase       					=    xmod_layer.self_attn_layer_norm.bias

						# intermediate
						__lowerCAmelCase       					=    layer.intermediate
						if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of intermediate weights do not match."   )
						__lowerCAmelCase       					=    xmod_layer.fca.weight
						__lowerCAmelCase       					=    xmod_layer.fca.bias

						# output
						__lowerCAmelCase       					=    layer.output
						if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
									raise AssertionError("Dimensions of feed-forward weights do not match."   )
						__lowerCAmelCase       					=    xmod_layer.fca.weight
						__lowerCAmelCase       					=    xmod_layer.fca.bias
						__lowerCAmelCase       					=    xmod_layer.final_layer_norm.weight
						__lowerCAmelCase       					=    xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									__lowerCAmelCase       					=    xmod_layer.adapter_layer_norm.weight
									__lowerCAmelCase       					=    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():
									__lowerCAmelCase       					=    bert_output.adapter_modules[lang_code]
									__lowerCAmelCase       					=    xmod_layer.adapter_modules[lang_code]
									__lowerCAmelCase       					=    from_adapter.fca.weight
									__lowerCAmelCase       					=    from_adapter.fca.bias
									__lowerCAmelCase       					=    from_adapter.fca.weight
									__lowerCAmelCase       					=    from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						__lowerCAmelCase       					=    xmod_sent_encoder.layer_norm.weight
						__lowerCAmelCase       					=    xmod_sent_encoder.layer_norm.bias

			if classification_head:
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"].dense.weight
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"].dense.bias
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"].out_proj.weight
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.dense.weight
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.dense.bias
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.layer_norm.weight
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.layer_norm.bias
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.weight
						__lowerCAmelCase       					=    xmod.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			__lowerCAmelCase       					=    xmod.encode(_UpperCamelCase   ).unsqueeze(0   )  # batch of size 1
			model.roberta.set_default_language(_UpperCamelCase   )

			__lowerCAmelCase       					=    model(_UpperCamelCase   )[0]
			if classification_head:
						__lowerCAmelCase       					=    xmod.model.classification_heads["mnli"](xmod.extract_features(_UpperCamelCase   )   )
			else:
						__lowerCAmelCase       					=    xmod.model(_UpperCamelCase					,				lang_id=[SAMPLE_LANGUAGE]   )[0]
			print(our_output.shape					,				their_output.shape   )
			__lowerCAmelCase       					=    torch.max(torch.abs(our_output - their_output   )   ).item()
			print(f"max_absolute_diff = {max_absolute_diff}"   )  # ~ 1e-7
			__lowerCAmelCase       					=    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__":
	A  :     str    					=    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."
	)
	A  :     Optional[Any]    					=    parser.parse_args()
	convert_xmod_checkpoint_to_pytorch(
	    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)








"""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


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

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

A  :     List[Any]    					=    2_5_0_0_0_4
A  :     int    					=    2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          =MBartaaTokenizer
							__UpperCAmelCase		:					Union[str, Any]          =MBartaaTokenizerFast
							__UpperCAmelCase		:					Optional[Any]          =True
							__UpperCAmelCase		:					Tuple          =True

							def     snake_case       (  self			):
										super().setUp()

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "<s>"
										__lowerCAmelCase       					=    0

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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(__a			)					,     10_54			)

							def     snake_case       (  self			):
										self.assertEqual(self.get_tokenizer().vocab_size					,     10_54			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    MBartaaTokenizer(__a					,     src_lang="en_XX"					,     tgt_lang="ro_RO"					,     keep_accents=__a			)

										__lowerCAmelCase       					=    tokenizer.tokenize("This is a test"			)
										self.assertListEqual(__a					,     ["▁This", "▁is", "▁a", "▁t", "est"]			)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__a			)					,     [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,     )

										__lowerCAmelCase       					=    tokenizer.tokenize("I was born in 92000, and this is falsé."			)
										self.assertListEqual(
										    __a					,     [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       					=    tokenizer.convert_tokens_to_ids(__a			)
										self.assertListEqual(
										    __a					,     [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]					,     )

										__lowerCAmelCase       					=    tokenizer.convert_ids_to_tokens(__a			)
										self.assertListEqual(
										    __a					,     [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			):
										# fmt: off
										__lowerCAmelCase       					=    {"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=__a					,     model_name="facebook/mbart-large-50"					,     revision="d3913889c59cd5c9e456b269c376325eabad57e2"					,     )



							def     snake_case       (  self			):
										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       					=    (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})"			):
																__lowerCAmelCase       					=    self.rust_tokenizer_class.from_pretrained(__a					,     **__a			)
																__lowerCAmelCase       					=    self.tokenizer_class.from_pretrained(__a					,     **__a			)

																__lowerCAmelCase       					=    tempfile.mkdtemp()

																__lowerCAmelCase       					=    tokenizer_r.save_pretrained(__a			)
																__lowerCAmelCase       					=    tokenizer_p.save_pretrained(__a			)

																# 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       					=    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f			)
																self.assertSequenceEqual(__a					,     __a			)

																# Checks everything loads correctly in the same way
																__lowerCAmelCase       					=    tokenizer_r.from_pretrained(__a			)
																__lowerCAmelCase       					=    tokenizer_p.from_pretrained(__a			)

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

																shutil.rmtree(__a			)

																# Save tokenizer rust, legacy_format=True
																__lowerCAmelCase       					=    tempfile.mkdtemp()

																__lowerCAmelCase       					=    tokenizer_r.save_pretrained(__a					,     legacy_format=__a			)
																__lowerCAmelCase       					=    tokenizer_p.save_pretrained(__a			)

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

																# Checks everything loads correctly in the same way
																__lowerCAmelCase       					=    tokenizer_r.from_pretrained(__a			)
																__lowerCAmelCase       					=    tokenizer_p.from_pretrained(__a			)

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

																shutil.rmtree(__a			)

																# Save tokenizer rust, legacy_format=False
																__lowerCAmelCase       					=    tempfile.mkdtemp()

																__lowerCAmelCase       					=    tokenizer_r.save_pretrained(__a					,     legacy_format=__a			)
																__lowerCAmelCase       					=    tokenizer_p.save_pretrained(__a			)

																# 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       					=    tokenizer_r.from_pretrained(__a			)
																__lowerCAmelCase       					=    tokenizer_p.from_pretrained(__a			)

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

																shutil.rmtree(__a			)
@require_torch
@require_sentencepiece
@require_tokenizers
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          ="""facebook/mbart-large-50-one-to-many-mmt"""
							__UpperCAmelCase		:					str          =[
							    """ 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		:					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.""",
							]
							__UpperCAmelCase		:					List[str]          =[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			):
										__lowerCAmelCase       					=    MBartaaTokenizer.from_pretrained(
										    cls.checkpoint_name					,     src_lang="en_XX"					,     tgt_lang="ro_RO"			)
										__lowerCAmelCase       					=    1
										return cls

							def     snake_case       (  self			):
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]					,     25_00_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]					,     25_00_04			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]					,     25_00_20			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]					,     25_00_38			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens					,     __a			)

							def     snake_case       (  self			):
										self.assertIn(__a					,     self.tokenizer.all_special_ids			)
										__lowerCAmelCase       					=    [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										__lowerCAmelCase       					=    self.tokenizer.decode(__a					,     skip_special_tokens=__a			)
										__lowerCAmelCase       					=    self.tokenizer.decode(generated_ids[1:]					,     skip_special_tokens=__a			)
										self.assertEqual(__a					,     __a			)
										self.assertNotIn(self.tokenizer.eos_token					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["this is gunna be a long sentence " * 20]
										assert isinstance(src_text[0]					,     __a			)
										__lowerCAmelCase       					=    10
										__lowerCAmelCase       					=    self.tokenizer(__a					,     max_length=__a					,     truncation=__a			).input_ids[0]
										self.assertEqual(ids[0]					,     __a			)
										self.assertEqual(ids[-1]					,     2			)
										self.assertEqual(len(__a			)					,     __a			)

							def     snake_case       (  self			):
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]			)					,     [25_00_53, 25_00_01]			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    tempfile.mkdtemp()
										__lowerCAmelCase       					=    self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__a			)
										__lowerCAmelCase       					=    MBartaaTokenizer.from_pretrained(__a			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,     __a			)

							@require_torch
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.tokenizer(self.src_text					,     text_target=self.tgt_text					,     padding=__a					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.tokenizer(
										    self.src_text					,     text_target=self.tgt_text					,     padding=__a					,     truncation=__a					,     max_length=len(self.expected_src_tokens			)					,     return_tensors="pt"					,     )
										__lowerCAmelCase       					=    shift_tokens_right(batch["labels"]					,     self.tokenizer.pad_token_id			)

										self.assertIsInstance(__a					,     __a			)

										self.assertEqual((2, 14)					,     batch.input_ids.shape			)
										self.assertEqual((2, 14)					,     batch.attention_mask.shape			)
										__lowerCAmelCase       					=    batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens					,     __a			)
										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			):
										__lowerCAmelCase       					=    self.tokenizer(self.src_text					,     padding=__a					,     truncation=__a					,     max_length=3					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    self.tokenizer(
										    text_target=self.tgt_text					,     padding=__a					,     truncation=__a					,     max_length=10					,     return_tensors="pt"			)
										__lowerCAmelCase       					=    targets["input_ids"]
										__lowerCAmelCase       					=    shift_tokens_right(__a					,     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			):
										__lowerCAmelCase       					=    self.tokenizer._build_translation_inputs(
										    "A test"					,     return_tensors="pt"					,     src_lang="en_XX"					,     tgt_lang="ar_AR"			)

										self.assertEqual(
										    nested_simplify(__a			)					,     {
										        # en_XX, A, test, EOS
										        "input_ids": [[25_00_04, 62, 30_34, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 25_00_01,
										    }					,     )




