

'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					MvpTokenizer
 __SCREAMING_SNAKE_CASE					:List[str]      	=					MvpTokenizerFast
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					True
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					filter_roberta_detectors



 def 			snake_case__  (   self		:   List[str]      ):
      super().setUp()
      __magic_name__						       =    [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      __magic_name__						       =    dict(zip(a__			,				range(len(a__      )      )      )      )
      __magic_name__						       =    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      __magic_name__						       =    {'''unk_token''': '''<unk>'''}

      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']      )
      __magic_name__						       =    os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''merges_file''']      )
      with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           fp.write(json.dumps(a__      ) + '''\n'''      )
      with open(self.merges_file			,				'''w'''			,				encoding='''utf-8'''      ) as fp:
           fp.write('''\n'''.join(a__      )      )



 def 			snake_case__  (   self		:   int			,				**a__		:   Tuple      ):
      kwargs.update(self.special_tokens_map      )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,				**a__      )



 def 			snake_case__  (   self		:   Optional[Any]			,				**a__		:   Optional[Any]      ):
      kwargs.update(self.special_tokens_map      )
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,				**a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Tuple      ):
      return "lower newer", "lower newer"



 @cached_property
 def 			snake_case__  (   self		:   Dict      ):
      return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''      )



 @cached_property
 def 			snake_case__  (   self		:   Any      ):
      return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''      )



 @require_torch
 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      __magic_name__						       =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__						       =    tokenizer(a__			,				max_length=len(a__      )			,				padding=a__			,				return_tensors='''pt'''      )
           self.assertIsInstance(a__			,				a__      )

           self.assertEqual((2, 9)			,				batch.input_ids.shape      )
           self.assertEqual((2, 9)			,				batch.attention_mask.shape      )
           __magic_name__						       =    batch.input_ids.tolist()[0]
           self.assertListEqual(a__			,				a__      )
           # Test that special tokens are reset



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__						       =    tokenizer(a__			,				padding=a__			,				return_tensors='''pt'''      )
           # check if input_ids are returned and no labels
           self.assertIn('''input_ids'''			,				a__      )
           self.assertIn('''attention_mask'''			,				a__      )
           self.assertNotIn('''labels'''			,				a__      )
           self.assertNotIn('''decoder_attention_mask'''			,				a__      )



 @require_torch
 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    [
          '''Summary of the text.''',
          '''Another summary.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__						       =    tokenizer(text_target=a__			,				max_length=32			,				padding='''max_length'''			,				return_tensors='''pt'''      )
           self.assertEqual(32			,				targets['''input_ids'''].shape[1]      )



 @require_torch
 def 			snake_case__  (   self		:   str      ):
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__						       =    tokenizer(
               ['''I am a small frog''' * 1024, '''I am a small frog''']			,				padding=a__			,				truncation=a__			,				return_tensors='''pt'''      )
           self.assertIsInstance(a__			,				a__      )
           self.assertEqual(batch.input_ids.shape			,				(2, 1024)      )



 @require_torch
 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    ['''A long paragraph for summarization.''']
      __magic_name__						       =    [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           __magic_name__						       =    tokenizer(a__			,				text_target=a__			,				return_tensors='''pt'''      )
           __magic_name__						       =    inputs['''input_ids''']
           __magic_name__						       =    inputs['''labels''']
           self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
           self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
           self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
           self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )



 def 			snake_case__  (   self		:   Tuple      ):
      pass



 def 			snake_case__  (   self		:   str      ):
      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(a__			,				**a__      )
                __magic_name__						       =    self.tokenizer_class.from_pretrained(a__			,				**a__      )
                __magic_name__						       =    '''A, <mask> AllenNLP sentence.'''
                __magic_name__						       =    tokenizer_r.encode_plus(a__			,				add_special_tokens=a__			,				return_token_type_ids=a__      )
                __magic_name__						       =    tokenizer_p.encode_plus(a__			,				add_special_tokens=a__			,				return_token_type_ids=a__      )

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']      )			,				sum(tokens_p['''token_type_ids''']      )      )

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']      ) / len(tokens_r['''attention_mask''']      )			,				sum(tokens_p['''attention_mask''']      ) / len(tokens_p['''attention_mask''']      )			,				)

                __magic_name__						       =    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
                __magic_name__						       =    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids''']			,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
                self.assertSequenceEqual(tokens_r['''input_ids''']			,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

                self.assertSequenceEqual(
                    a__			,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
                self.assertSequenceEqual(
                    a__			,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )









'''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   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    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.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

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

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      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'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






