
"""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   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			1






       @register_to_config
       def __init__(  self :							List[Any]       ,      UpperCAmelCase_ :							int = 1_000       ,      UpperCAmelCase_ :							Optional[Union[np.ndarray, List[float]]] = None			)->  Optional[int]:







              """simple docstring"""
              # set `betas`, `alphas`, `timesteps`
              self.set_timesteps(UpperCAmelCase_			)

              # standard deviation of the initial noise distribution
              UpperCamelCase					 =		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					 =		4

              # running values
              UpperCamelCase					 =		[]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Union[str, torch.device] = None			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		num_inference_steps
              UpperCamelCase					 =		torch.linspace(1       ,      0       ,      num_inference_steps + 1			)[:-1]
              UpperCamelCase					 =		torch.cat([steps, torch.tensor([0.0]			)]			)

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

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

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

              UpperCamelCase					 =		[]






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      UpperCAmelCase_ :							bool = True       ,      )->  Union[SchedulerOutput, Tuple]:







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

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

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

              UpperCamelCase					 =		self._get_prev_sample(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							torch.FloatTensor       ,      *UpperCAmelCase_ :							Dict       ,      **UpperCAmelCase_ :							Optional[Any]			)->  torch.FloatTensor:







              """simple docstring"""
              return sample






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.alphas[timestep_index]
              UpperCamelCase					 =		self.betas[timestep_index]

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

              UpperCamelCase					 =		(sample - sigma * ets) / max(UpperCAmelCase_       ,      1e-8			)
              UpperCamelCase					 =		next_alpha * pred + ets * next_sigma

              return prev_sample






       def __len__(  self :							Tuple			)->  Any:







              """simple docstring"""
              return self.config.num_train_timesteps







"""simple docstring"""


import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			FunnelTokenizer
       UpperCamelCase_		:					Any   =			FunnelTokenizerFast
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Tuple   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		[
                  "<unk>",
                  "<cls>",
                  "<sep>",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              UpperCamelCase					 =		os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]			)
              with open(self.vocab_file       ,      "w"       ,      encoding="utf-8"			) as vocab_writer:
                     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      **UpperCAmelCase_ :							Optional[Any]			)->  str:







              """simple docstring"""
              return FunnelTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      **UpperCAmelCase_ :							Any			)->  Union[str, Any]:







              """simple docstring"""
              return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"UNwant\u00E9d,running"
              UpperCamelCase					 =		"unwanted, running"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file			)

              UpperCamelCase					 =		tokenizer.tokenize("UNwant\u00E9d,running"			)
              self.assertListEqual(UpperCAmelCase_       ,      ["un", "##want", "##ed", ",", "runn", "##ing"]			)
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      [7, 4, 5, 10, 8, 9]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizers(do_lower_case=UpperCAmelCase_			)
              for tokenizer in tokenizers:
                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"			)
                     UpperCamelCase					 =		len(inputs["input_ids"]			) - 1
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len			)

                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"       ,      "UNwant\u00E9d,running"			)
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len + [1] * sentence_len			)





