





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


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

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

UpperCamelCase__      =25_0004
UpperCamelCase__      =25_0020





@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase__( __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    MBartaaTokenizer
       __snake_case 			=    MBartaaTokenizerFast
       __snake_case 			=    True
       __snake_case 			=    True




       def     UpperCamelCase_			(      self							)			->     Any:
            super().setUp()

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




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"<s>"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0

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




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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     UpperCamelCase_			(      self							)			->     Any:
            self.assertEqual(self.get_tokenizer().vocab_size    ,						1_0_5_4							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							MBartaaTokenizer(__lowerCamelCase    ,						src_lang="en_XX"    ,						tgt_lang="ro_RO"    ,						keep_accents=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							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]]    ,						)

            _SCREAMING_SNAKE_CASE       :	Any						    =							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", "."]    ,						)
            _SCREAMING_SNAKE_CASE       :	Any						    =							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]
                ]    ,						)

            _SCREAMING_SNAKE_CASE       :	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>", "."]    ,						)




       @slow
       def     UpperCamelCase_			(      self							)			->     List[Any]:
            # fmt: off
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							{"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     UpperCamelCase_			(      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

            _SCREAMING_SNAKE_CASE       :	str						    =							(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})"""							):
                      _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.rust_tokenizer_class.from_pretrained(__lowerCamelCase    ,						**__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.tokenizer_class.from_pretrained(__lowerCamelCase    ,						**__lowerCamelCase							)

                      _SCREAMING_SNAKE_CASE       :	int						    =							tempfile.mkdtemp()

                      _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tokenizer_r.save_pretrained(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							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							)							)
                      _SCREAMING_SNAKE_CASE       :	Any						    =							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
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							tokenizer_r.from_pretrained(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	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
                      _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tempfile.mkdtemp()

                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tokenizer_r.save_pretrained(__lowerCamelCase    ,						legacy_format=__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Any						    =							tokenizer_p.save_pretrained(__lowerCamelCase							)

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

                      # Checks everything loads correctly in the same way
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							tokenizer_r.from_pretrained(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	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							)							)

                      shutil.rmtree(__lowerCamelCase							)

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

                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							tokenizer_r.save_pretrained(__lowerCamelCase    ,						legacy_format=__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	int						    =							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
                      _SCREAMING_SNAKE_CASE       :	str						    =							tokenizer_r.from_pretrained(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Optional[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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


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




       def     UpperCamelCase_			(      self							)			->     Tuple:
            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     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
            self.assertListEqual(self.expected_src_tokens    ,						__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            self.assertIn(__lowerCamelCase    ,						self.tokenizer.all_special_ids							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[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]
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.tokenizer.decode(__lowerCamelCase    ,						skip_special_tokens=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.tokenizer.decode(generated_ids[1:]    ,						skip_special_tokens=__lowerCamelCase							)
            self.assertEqual(__lowerCamelCase    ,						__lowerCamelCase							)
            self.assertNotIn(self.tokenizer.eos_token    ,						__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							["this is gunna be a long sentence " * 2_0]
            assert isinstance(src_text[0]    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							1_0
            _SCREAMING_SNAKE_CASE       :	str						    =							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     UpperCamelCase_			(      self							)			->     Any:
            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]							)    ,						[2_5_0_0_5_3, 2_5_0_0_0_1]							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.tokenizer.fairseq_tokens_to_ids
            self.tokenizer.save_pretrained(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							MBartaaTokenizer.from_pretrained(__lowerCamelCase							)
            self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,						__lowerCamelCase							)




       @require_torch
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.tokenizer(self.src_text    ,						text_target=self.tgt_text    ,						padding=__lowerCamelCase    ,						return_tensors="pt"							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							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     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.tokenizer(
                self.src_text    ,						text_target=self.tgt_text    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=len(self.expected_src_tokens							)    ,						return_tensors="pt"    ,						)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							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     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.tokenizer(self.src_text    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=3    ,						return_tensors="pt"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.tokenizer(
                text_target=self.tgt_text    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=1_0    ,						return_tensors="pt"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							targets["input_ids"]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							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     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							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,
                }    ,						)






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							len(__lowerCamelCase							) - 1




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     list[float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]
            for i in range(len(self.list_of_points							)							):
                 # basis function for each i
                 output_values.append(
                     comb(self.degree    ,						__lowerCamelCase							) * ((1 - t) ** (self.degree - i)) * (t**i)							)
            # the basis must sum up to 1 for it to produce a valid Bezier curve.
            assert round(sum(__lowerCamelCase							)    ,						5							) == 1
            return output_values




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     tuple[float, float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.basis_function(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							0.0
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0.0
            for i in range(len(self.list_of_points							)							):
                 # For all points, sum up the product of i-th basis function and i-th point.
                 x += basis_function[i] * self.list_of_points[i][0]
                 y += basis_function[i] * self.list_of_points[i][1]
            return (x, y)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase = 0.01							)			->     int:
            from matplotlib import pyplot as plt  # type: ignore

            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # x coordinates of points to plot
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]  # y coordinates of points to plot

            _SCREAMING_SNAKE_CASE       :	Dict						    =							0.0
            while t <= 1:
                 _SCREAMING_SNAKE_CASE       :	str						    =							self.bezier_curve_function(__lowerCamelCase							)
                 to_plot_x.append(value[0]							)
                 to_plot_y.append(value[1]							)
                 t += step_size

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[i[0] for i in self.list_of_points]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[i[1] for i in self.list_of_points]

            plt.plot(
                __lowerCamelCase    ,						__lowerCamelCase    ,						color="blue"    ,						label="Curve of Degree " + str(self.degree							)    ,						)
            plt.scatter(__lowerCamelCase    ,						__lowerCamelCase    ,						color="red"    ,						label="Control Points"							)
            plt.legend()
            plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3