




'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		LEDTokenizer
					_UpperCamelCase							=		LEDTokenizerFast
					_UpperCamelCase							=		True



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

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



					def     __snake_case						(	self     :						str      ,       **_lowercase     :						Optional[Any])    ->							Any:
								kwargs.update(self.special_tokens_map)
								return self.tokenizer_class.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       **_lowercase     :						int)    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Dict)    ->							Dict:
								return "lower newer", "lower newer"



					@cached_property
					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								return LEDTokenizer.from_pretrained('allenai/led-base-16384')



					@cached_property
					def     __snake_case						(	self     :						int)    ->							Union[str, Any]:
								return LEDTokenizerFast.from_pretrained('allenai/led-base-16384')



					@require_torch
					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								A_									= [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

											self.assertEqual((2, 9)      ,       batch.input_ids.shape)
											self.assertEqual((2, 9)      ,       batch.attention_mask.shape)
											A_									= batch.input_ids.tolist()[0]
											self.assertListEqual(_lowercase      ,       _lowercase)



					@require_torch
					def     __snake_case						(	self     :						int)    ->							Union[str, Any]:
								A_									= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											A_									= tokenizer(_lowercase      ,       padding=_lowercase      ,       return_tensors='pt')
											self.assertIn('input_ids'      ,       _lowercase)
											self.assertIn('attention_mask'      ,       _lowercase)
											self.assertNotIn('labels'      ,       _lowercase)
											self.assertNotIn('decoder_attention_mask'      ,       _lowercase)



					@require_torch
					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= [
								    'Summary of the text.',
								    'Another summary.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											A_									= tokenizer(text_target=_lowercase      ,       max_length=32      ,       padding='max_length'      ,       return_tensors='pt')
											self.assertEqual(32      ,       targets['input_ids'].shape[1])



					@require_torch
					def     __snake_case						(	self     :						Dict)    ->							List[Any]:
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											A_									= tokenizer(
											    ['I am a small frog' * 1_024, 'I am a small frog']      ,       padding=_lowercase      ,       truncation=_lowercase      ,       return_tensors='pt')
											self.assertIsInstance(_lowercase      ,       _lowercase)
											self.assertEqual(batch.input_ids.shape      ,       (2, 5_122))



					@require_torch
					def     __snake_case						(	self     :						int)    ->							List[str]:
								A_									= ['A long paragraph for summarization.']
								A_									= [
								    'Summary of the text.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											A_									= tokenizer(_lowercase      ,       return_tensors='pt')
											A_									= tokenizer(text_target=_lowercase      ,       return_tensors='pt')
											A_									= inputs['input_ids']
											A_									= targets['input_ids']
											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())



					@require_torch
					def     __snake_case						(	self     :						str)    ->							Dict:
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											A_									= ['Summary of the text.', 'Another summary.']
											A_									= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											A_									= tokenizer(_lowercase      ,       padding=_lowercase)
											A_									= [[0] * len(_lowercase) for x in encoded_output['input_ids']]
											A_									= tokenizer.pad(_lowercase)
											self.assertSequenceEqual(outputs['global_attention_mask']      ,       _lowercase)



					def     __snake_case						(	self     :						Optional[int])    ->							List[str]:
								pass



					def     __snake_case						(	self     :						List[Any])    ->							int:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
														A_									= self.rust_tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)
														A_									= self.tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)
														A_									= 'A, <mask> AllenNLP sentence.'
														A_									= tokenizer_r.encode_plus(_lowercase      ,       add_special_tokens=_lowercase      ,       return_token_type_ids=_lowercase)
														A_									= tokenizer_p.encode_plus(_lowercase      ,       add_special_tokens=_lowercase      ,       return_token_type_ids=_lowercase)
														self.assertEqual(sum(tokens_r['token_type_ids'])      ,       sum(tokens_p['token_type_ids']))
														self.assertEqual(
														    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])      ,       sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])      ,       )

														A_									= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
														A_									= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])
														self.assertSequenceEqual(tokens_p['input_ids']      ,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
														self.assertSequenceEqual(tokens_r['input_ids']      ,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

														self.assertSequenceEqual(
														    _lowercase      ,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
														self.assertSequenceEqual(
														    _lowercase      ,       ['<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   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

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

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

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

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

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

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								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')

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

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

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

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

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

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

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




