

"""simple docstring"""




import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							ProphetNetTokenizer
  snake_case_												=							False

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   super().setUp()

   UpperCAmelCase__       :	Any        =       [
       """[UNK]""",
       """[CLS]""",
       """[SEP]""",
       """[PAD]""",
       """[MASK]""",
       """want""",
       """##want""",
       """##ed""",
       """wa""",
       """un""",
       """runn""",
       """##ing""",
       """,""",
       """low""",
       """lowest""",
   ]
   UpperCAmelCase__       :	int        =       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  __lowercase							(	self		:      Any       ,A		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       """UNwant\u00E9d,running"""
   UpperCAmelCase__       :	List[str]        =       """unwanted, running"""
   return input_text, output_text

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       self.tokenizer_class(self.vocab_file     )

   UpperCAmelCase__       :	int        =       tokenizer.tokenize("""UNwant\u00E9d,running"""     )
   self.assertListEqual(__a       ,["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
   self.assertListEqual(tokenizer.convert_tokens_to_ids(__a     )       ,[9, 6, 7, 12, 10, 11]     )

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       BasicTokenizer()

   self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""     )       ,["""ah""", """\u535A""", """\u63A8""", """zz"""]     )

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       BasicTokenizer(do_lower_case=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )       ,["""hello""", """!""", """how""", """are""", """you""", """?"""]     )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,["""hello"""]     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       BasicTokenizer(do_lower_case=__a       ,strip_accents=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,["""hällo""", """!""", """how""", """are""", """you""", """?"""]     )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,["""h\u00E9llo"""]     )

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       BasicTokenizer(do_lower_case=__a       ,strip_accents=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,["""hello"""]     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       BasicTokenizer(do_lower_case=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )       ,["""hello"""]     )

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       BasicTokenizer(do_lower_case=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )       ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       BasicTokenizer(do_lower_case=__a       ,strip_accents=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       BasicTokenizer(do_lower_case=__a       ,strip_accents=__a     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )       ,["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       BasicTokenizer(do_lower_case=__a       ,never_split=["""[UNK]"""]     )

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""     )       ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

   UpperCAmelCase__       :	List[str]        =       {}
   for i, token in enumerate(__a     ):
    UpperCAmelCase__       :	int        =       i
   UpperCAmelCase__       :	List[Any]        =       WordpieceTokenizer(vocab=__a       ,unk_token="""[UNK]"""     )

   self.assertListEqual(tokenizer.tokenize(""""""     )       ,[]     )

   self.assertListEqual(tokenizer.tokenize("""unwanted running"""     )       ,["""un""", """##want""", """##ed""", """runn""", """##ing"""]     )

   self.assertListEqual(tokenizer.tokenize("""unwantedX running"""     )       ,["""[UNK]""", """runn""", """##ing"""]     )

  @require_torch
  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""     )

   UpperCAmelCase__       :	Union[str, Any]        =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   UpperCAmelCase__       :	Dict        =       [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
   UpperCAmelCase__       :	int        =       tokenizer(__a       ,padding=__a       ,return_tensors="""pt"""     )
   self.assertIsInstance(__a       ,__a     )
   UpperCAmelCase__       :	int        =       list(batch.input_ids.numpy()[0]     )
   self.assertListEqual(__a       ,__a     )

   self.assertEqual((2, 9)       ,batch.input_ids.shape     )
   self.assertEqual((2, 9)       ,batch.attention_mask.shape     )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   self.assertTrue(_is_whitespace(""" """     )     )
   self.assertTrue(_is_whitespace("""\t"""     )     )
   self.assertTrue(_is_whitespace("""\r"""     )     )
   self.assertTrue(_is_whitespace("""\n"""     )     )
   self.assertTrue(_is_whitespace("""\u00A0"""     )     )

   self.assertFalse(_is_whitespace("""A"""     )     )
   self.assertFalse(_is_whitespace("""-"""     )     )

  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   self.assertTrue(_is_control("""\u0005"""     )     )

   self.assertFalse(_is_control("""A"""     )     )
   self.assertFalse(_is_control(""" """     )     )
   self.assertFalse(_is_control("""\t"""     )     )
   self.assertFalse(_is_control("""\r"""     )     )

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   self.assertTrue(_is_punctuation("""-"""     )     )
   self.assertTrue(_is_punctuation("""$"""     )     )
   self.assertTrue(_is_punctuation("""`"""     )     )
   self.assertTrue(_is_punctuation("""."""     )     )

   self.assertFalse(_is_punctuation("""A"""     )     )
   self.assertFalse(_is_punctuation(""" """     )     )




  @slow
  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""     )

   UpperCAmelCase__       :	Optional[int]        =       tokenizer.encode("""sequence builders"""       ,add_special_tokens=__a     )
   UpperCAmelCase__       :	str        =       tokenizer.encode("""multi-sequence build"""       ,add_special_tokens=__a     )

   UpperCAmelCase__       :	Any        =       tokenizer.build_inputs_with_special_tokens(__a     )
   UpperCAmelCase__       :	str        =       tokenizer.build_inputs_with_special_tokens(__a       ,__a     )

   assert encoded_sentence == text + [102]
   assert encoded_pair == text + [102] + text_a + [102]






'''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 ( UpperCAmelCase							,						UpperCAmelCase ):
							a_										=   1





							@register_to_config
							def __init__(   self					:				Tuple  ,		__a					:				int = 1_0_0_0  ,		__a					:				Optional[Union[np.ndarray, List[float]]] = None						)				->						int:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(__a						)

								# 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    snake_case__    (   self					:				str  ,		__a					:				int  ,		__a					:				Union[str, torch.device] = None						)				->						str:
								__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(__a						)

								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		__a					:				int  ,		__a					:				torch.FloatTensor  ,		__a					:				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'''						)

								__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(__a						)

								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      					=      (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
								else:
									__UpperCAmelCase      					=      (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

								__UpperCAmelCase      					=      self._get_prev_sample(__a  ,		__a  ,		__a  ,		__a						)

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		*__a					:				Any  ,		**__a					:				Optional[int]						)				->						torch.FloatTensor:
								return sample





							def    snake_case__    (   self					:				int  ,		__a					:				List[Any]  ,		__a					:				Any  ,		__a					:				int  ,		__a					:				Union[str, Any]						)				->						Optional[Any]:
								__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(__a  ,		1e-8						)
								__UpperCAmelCase      					=      next_alpha * pred + ets * next_sigma

								return prev_sample






							def __len__(   self					:				Dict						)				->						str:
								return self.config.num_train_timesteps
