'''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       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ProphetNetTokenizer
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Any:
        super().setUp()

        lowercase__    :   Tuple						= [
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        lowercase__    :   List[str]						= 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Union[str, Any]						= '''UNwant\u00E9d,running'''
        lowercase__    :   List[Any]						= '''unwanted, running'''
        return input_text, output_text

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[str]						= self.tokenizer_class(self.vocab_file			)

        lowercase__    :   Union[str, Any]						= tokenizer.tokenize('''UNwant\u00E9d,running'''			)
        self.assertListEqual(__lowerCAmelCase     ,       ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       [9, 6, 7, 12, 10, 11]			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= BasicTokenizer()

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

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   str						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       never_split=['''[UNK]''']			)

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

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        lowercase__    :   Union[str, Any]						= {}
        for i, token in enumerate(__lowerCAmelCase			):
         lowercase__    :   List[Any]						= i
        lowercase__    :   Dict						= WordpieceTokenizer(vocab=__lowerCAmelCase     ,       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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Union[str, Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   Optional[int]						= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        lowercase__    :   Dict						= [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
        lowercase__    :   int						= tokenizer(__lowerCAmelCase     ,       padding=__lowerCAmelCase     ,       return_tensors='''pt'''			)
        self.assertIsInstance(__lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Optional[int]						= list(batch.input_ids.numpy()[0]			)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

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

       def _lowerCAmelCase(						self			)  ->  str:
        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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        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 _lowerCAmelCase(						self			)  ->  Dict:
        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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   List[Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   List[str]						= tokenizer.encode('''sequence builders'''     ,       add_special_tokens=__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= tokenizer.encode('''multi-sequence build'''     ,       add_special_tokens=__lowerCAmelCase			)

        lowercase__    :   Tuple						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
        lowercase__    :   List[str]						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase     ,       __lowerCAmelCase			)

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


'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCAmelCase					) / alpha_bar_fn(UpperCAmelCase					)		,					UpperCAmelCase					)					)
 return torch.tensor(UpperCAmelCase		,					dtype=torch.floataa					)





class       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 1 - alpha_prod_t / alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 1 - beta

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= model_output
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             ''' for the UnCLIPScheduler.'''			)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -self.config.clip_sample_range     ,       self.config.clip_sample_range			)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (0.5 * variance).exp()
         else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              ''' for the UnCLIPScheduler.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

        lowercase__    :   int						= (1 - alphas_cumprod[timesteps]) ** 0.5
        lowercase__    :   List[Any]						= sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   int						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

