'''simple docstring'''


import shutil
import tempfile
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__					:      int				      =  get_tests_dir('''fixtures/test_sentencepiece.model''')


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

snake_case__					:      Optional[Any]				      =  25_0004
snake_case__					:      str				      =  25_0020






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







  '''simple docstring'''






  lowerCamelCase_		:int						   =		MBartTokenizer
  lowerCamelCase_		:Any						   =		MBartTokenizerFast
  lowerCamelCase_		:int						   =		True
  lowerCamelCase_		:str						   =		True


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       super().setUp()

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				MBartTokenizer(snake_case_				,		keep_accents=snake_case_						)

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

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(snake_case_						)				,		[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]				,		)

       UpperCAmelCase_							:       Optional[int]       =				tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
       self.assertListEqual(
           snake_case_				,		[
               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',
               '.',
           ]				,		)
       UpperCAmelCase_							:       List[Any]       =				tokenizer.convert_tokens_to_ids(snake_case_						)
       self.assertListEqual(
           snake_case_				,		[
               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]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]				,		)

       UpperCAmelCase_							:       str       =				tokenizer.convert_ids_to_tokens(snake_case_						)
       self.assertListEqual(
           snake_case_				,		[
               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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       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

       UpperCAmelCase_							:       Tuple       =				(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})'''						):
                 UpperCAmelCase_							:       Any       =				self.rust_tokenizer_class.from_pretrained(snake_case_				,		**snake_case_						)
                 UpperCAmelCase_							:       Optional[Any]       =				self.tokenizer_class.from_pretrained(snake_case_				,		**snake_case_						)

                 UpperCAmelCase_							:       List[Any]       =				tempfile.mkdtemp()

                 UpperCAmelCase_							:       Optional[Any]       =				tokenizer_r.save_pretrained(snake_case_						)
                 UpperCAmelCase_							:       List[str]       =				tokenizer_p.save_pretrained(snake_case_						)

                 # 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						)						)
                 UpperCAmelCase_							:       Dict       =				tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f						)
                 self.assertSequenceEqual(snake_case_				,		snake_case_						)

                 # Checks everything loads correctly in the same way
                 UpperCAmelCase_							:       List[str]       =				tokenizer_r.from_pretrained(snake_case_						)
                 UpperCAmelCase_							:       int       =				tokenizer_p.from_pretrained(snake_case_						)

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

                 shutil.rmtree(snake_case_						)

                 # Save tokenizer rust, legacy_format=True
                 UpperCAmelCase_							:       str       =				tempfile.mkdtemp()

                 UpperCAmelCase_							:       Optional[Any]       =				tokenizer_r.save_pretrained(snake_case_				,		legacy_format=snake_case_						)
                 UpperCAmelCase_							:       Any       =				tokenizer_p.save_pretrained(snake_case_						)

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

                 # Checks everything loads correctly in the same way
                 UpperCAmelCase_							:       Union[str, Any]       =				tokenizer_r.from_pretrained(snake_case_						)
                 UpperCAmelCase_							:       Optional[Any]       =				tokenizer_p.from_pretrained(snake_case_						)

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

                 shutil.rmtree(snake_case_						)

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

                 UpperCAmelCase_							:       str       =				tokenizer_r.save_pretrained(snake_case_				,		legacy_format=snake_case_						)
                 UpperCAmelCase_							:       Optional[int]       =				tokenizer_p.save_pretrained(snake_case_						)

                 # 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
                 UpperCAmelCase_							:       Tuple       =				tokenizer_r.from_pretrained(snake_case_						)
                 UpperCAmelCase_							:       Any       =				tokenizer_p.from_pretrained(snake_case_						)

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

                 shutil.rmtree(snake_case_						)







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







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		'''facebook/mbart-large-en-ro'''
  lowerCamelCase_		:Any						   =		[
      ''' 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.''',
  ]
  lowerCamelCase_		: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.''',
  ]
  lowerCamelCase_		:Dict						   =		[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]


  @classmethod
  def _UpperCamelCase					(			cls						):




       '''simple docstring'''

       UpperCAmelCase_							:       MBartTokenizer       =				MBartTokenizer.from_pretrained(
           cls.checkpoint_name				,		src_lang='en_XX'				,		tgt_lang='ro_RO'						)
       UpperCAmelCase_							:       Optional[Any]       =				1
       return cls


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
       self.assertListEqual(self.expected_src_tokens				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.assertIn(snake_case_				,		self.tokenizer.all_special_ids						)
       UpperCAmelCase_							:       Tuple       =				[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]
       UpperCAmelCase_							:       Tuple       =				self.tokenizer.decode(snake_case_				,		skip_special_tokens=snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.tokenizer.decode(generated_ids[1:]				,		skip_special_tokens=snake_case_						)
       self.assertEqual(snake_case_				,		snake_case_						)
       self.assertNotIn(self.tokenizer.eos_token				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				['this is gunna be a long sentence ' * 2_0]
       assert isinstance(src_text[0]				,		snake_case_						)
       UpperCAmelCase_							:       Any       =				1_0
       UpperCAmelCase_							:       Optional[Any]       =				self.tokenizer(snake_case_				,		max_length=snake_case_				,		truncation=snake_case_						).input_ids[0]
       self.assertEqual(ids[-2]				,		2						)
       self.assertEqual(ids[-1]				,		snake_case_						)
       self.assertEqual(len(snake_case_						)				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)				,		[2_5_0_0_2_6, 2_5_0_0_0_1]						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				tempfile.mkdtemp()
       UpperCAmelCase_							:       Optional[int]       =				self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				MBartTokenizer.from_pretrained(snake_case_						)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,		snake_case_						)


  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.tokenizer(self.src_text				,		text_target=self.tgt_text				,		padding=snake_case_				,		return_tensors='pt'						)
       UpperCAmelCase_							:       Dict       =				shift_tokens_right(batch['labels']				,		self.tokenizer.pad_token_id						)

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


  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.tokenizer(
           self.src_text				,		text_target=self.tgt_text				,		padding=snake_case_				,		truncation=snake_case_				,		max_length=len(self.expected_src_tokens						)				,		return_tensors='pt'				,		)

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

       self.assertIsInstance(snake_case_				,		snake_case_						)

       self.assertEqual((2, 1_4)				,		batch.input_ids.shape						)
       self.assertEqual((2, 1_4)				,		batch.attention_mask.shape						)
       UpperCAmelCase_							:       Optional[int]       =				batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens				,		snake_case_						)
       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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.tokenizer(self.src_text				,		padding=snake_case_				,		truncation=snake_case_				,		max_length=3				,		return_tensors='pt'						)
       UpperCAmelCase_							:       int       =				self.tokenizer(
           text_target=self.tgt_text				,		padding=snake_case_				,		truncation=snake_case_				,		max_length=1_0				,		return_tensors='pt'						)
       UpperCAmelCase_							:       List[Any]       =				targets['input_ids']
       UpperCAmelCase_							:       int       =				shift_tokens_right(snake_case_				,		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						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.tokenizer._build_translation_inputs(
           'A test'				,		return_tensors='pt'				,		src_lang='en_XX'				,		tgt_lang='ar_AR'						)

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







'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1


  @register_to_config
  def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




       '''simple docstring'''

       self.set_timesteps(snake_case_						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase_							:       Union[str, Any]       =				1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       UpperCAmelCase_							:       int       =				4

       # running values
       UpperCAmelCase_							:       str       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
       UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
       UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

       if self.config.trained_betas is not None:
            UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
       else:
            UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

       UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

       UpperCAmelCase_							:       str       =				(torch.atana(self.betas				,		self.alphas						) / math.pi * 2)[:-1]
       UpperCAmelCase_							:       str       =				timesteps.to(snake_case_						)

       UpperCAmelCase_							:       Any       =				[]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = True				,		):




       '''simple docstring'''

       if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

       UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
       UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

       UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(snake_case_						)

       if len(self.ets						) == 1:
            UpperCAmelCase_							:       Tuple       =				self.ets[-1]
       elif len(self.ets						) == 2:
            UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets						) == 3:
            UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

       UpperCAmelCase_							:       Union[str, Any]       =				self._get_prev_sample(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return sample


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
       UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

       UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
       UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

       UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
       UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

       return prev_sample






  def __len__(			self						):




       '''simple docstring'''

       return self.config.num_train_timesteps






