
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


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


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

_A  =	25_00_04
_A  =	25_00_20





@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=MBartTokenizer
				_lowercase     						=MBartTokenizerFast
				_lowercase     						=True
				_lowercase     						=True




				def 	__a		(   self	)							-> int:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase_         =      MBartTokenizer(_UpperCamelCase							,							keep_accents=_UpperCamelCase	)
							tokenizer.save_pretrained(self.tmpdirname	)




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      MBartTokenizer(_UpperCamelCase							,							keep_accents=_UpperCamelCase	)

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

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

							lowerCAmelCase_         =      tokenizer.tokenize("I was born in 92000, and this is falsé."	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        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(_UpperCamelCase	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        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_         =      tokenizer.convert_ids_to_tokens(_UpperCamelCase	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        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 	__a		(   self	)							-> Union[str, 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_         =      (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_         =      self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)
													lowerCAmelCase_         =      self.tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)

													lowerCAmelCase_         =      tempfile.mkdtemp()

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

													# 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(_UpperCamelCase							,							_UpperCamelCase	)

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

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

													shutil.rmtree(_UpperCamelCase	)

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

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase							,							legacy_format=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

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

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

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

													shutil.rmtree(_UpperCamelCase	)

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

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase							,							legacy_format=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

													# 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(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.from_pretrained(_UpperCamelCase	)

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

													shutil.rmtree(_UpperCamelCase	)





@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						='''facebook/mbart-large-en-ro'''
				_lowercase     						=[
				    ''' 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.''',
				]
				_lowercase     						=[
				    '''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.''',
				]
				_lowercase     						=[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]




				@classmethod
				def 	__a		(   cls	)							-> Optional[int]:
							lowerCAmelCase_         =      MBartTokenizer.from_pretrained(
							    cls.checkpoint_name							,							src_lang="en_XX"							,							tgt_lang="ro_RO"	)
							lowerCAmelCase_         =      1
							return cls




				def 	__a		(   self	)							-> Dict:
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]							,							250_001	)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]							,							250_004	)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]							,							250_020	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
							self.assertListEqual(self.expected_src_tokens							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Optional[int]:
							self.assertIn(_UpperCamelCase							,							self.tokenizer.all_special_ids	)
							lowerCAmelCase_         =      [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
							lowerCAmelCase_         =      self.tokenizer.decode(_UpperCamelCase							,							skip_special_tokens=_UpperCamelCase	)
							lowerCAmelCase_         =      self.tokenizer.decode(generated_ids[1:]							,							skip_special_tokens=_UpperCamelCase	)
							self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)
							self.assertNotIn(self.tokenizer.eos_token							,							_UpperCamelCase	)




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




				def 	__a		(   self	)							-> int:
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)							,							[250_026, 250_001]	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      tempfile.mkdtemp()
							lowerCAmelCase_         =      self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(_UpperCamelCase	)
							lowerCAmelCase_         =      MBartTokenizer.from_pretrained(_UpperCamelCase	)
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,							_UpperCamelCase	)




				@require_torch
				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.tokenizer(self.src_text							,							text_target=self.tgt_text							,							padding=_UpperCamelCase							,							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][-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 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.tokenizer(
							    self.src_text							,							text_target=self.tgt_text							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase							,							max_length=len(self.expected_src_tokens	)							,							return_tensors="pt"							,							)

							lowerCAmelCase_         =      shift_tokens_right(batch["labels"]							,							self.tokenizer.pad_token_id	)

							self.assertIsInstance(_UpperCamelCase							,							_UpperCamelCase	)

							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							,							_UpperCamelCase	)
							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 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.tokenizer(self.src_text							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase							,							max_length=3							,							return_tensors="pt"	)
							lowerCAmelCase_         =      self.tokenizer(
							    text_target=self.tgt_text							,							padding=_UpperCamelCase							,							truncation=_UpperCamelCase							,							max_length=10							,							return_tensors="pt"	)
							lowerCAmelCase_         =      targets["input_ids"]
							lowerCAmelCase_         =      shift_tokens_right(_UpperCamelCase							,							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 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.tokenizer._build_translation_inputs(
							    "A test"							,							return_tensors="pt"							,							src_lang="en_XX"							,							tgt_lang="ar_AR"	)

							self.assertEqual(
							    nested_simplify(_UpperCamelCase	)							,							{
							        # A, test, EOS, en_XX
							        "input_ids": [[62, 3_034, 2, 250_004]],
							        "attention_mask": [[1, 1, 1, 1]],
							        # ar_AR
							        "forced_bos_token_id": 250_001,
							    }							,							)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''ibert'''




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=12							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=2							,							_UpperCamelCase="absolute"							,							_UpperCamelCase=False							,							_UpperCamelCase="none"							,							**_UpperCamelCase							,							)							-> List[str]:
							super().__init__(pad_token_id=_UpperCamelCase							,							bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      position_embedding_type
							lowerCAmelCase_         =      quant_mode
							lowerCAmelCase_         =      force_dequant





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_         =      {0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]	)



