



"""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
class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: torch.FloatTensor
     _lowerCamelCase					: torch.FloatTensor



def   a_	(			__a		,							__a=0.9_99		,							__a="cosine"		,							):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__a      ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__a      ):
               return math.exp(t * -12.0      )

     else:
          raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )

     A__				=   []
     for i in range(__a      ):
          A__				=   i / num_diffusion_timesteps
          A__				=   (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__a      ) / alpha_bar_fn(__a      )		,							__a      )      )
     return torch.tensor(__a		,							dtype=torch.floataa      )

class       UpperCamelCase  (					a      , a				):





     """simple docstring"""






     _lowerCamelCase					: str				   =1





     @register_to_config
     def __init__(					self :							Any	,      _lowerCamelCase :							int = 1_0_0_0	,      _lowerCamelCase :							float = 0.0_001	,      _lowerCamelCase :							float = 0.02	,      _lowerCamelCase :							str = "linear"	,      _lowerCamelCase :							float = 0.0	,      _lowerCamelCase :							Optional[np.ndarray] = None	,      _lowerCamelCase :							bool = True	,      ):
          if trained_betas is not None:
               A__				=   torch.from_numpy(_lowerCamelCase )
          elif beta_schedule == "linear":
               A__				=   torch.linspace(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               A__				=   (
                   torch.linspace(beta_start**0.5	,      beta_end**0.5	,      _lowerCamelCase	,      dtype=torch.floataa ) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               A__				=   betas_for_alpha_bar(_lowerCamelCase )
          elif beta_schedule == "sigmoid":
               # GeoDiff sigmoid schedule
               A__				=   torch.linspace(-6	,      6	,      _lowerCamelCase )
               A__				=   torch.sigmoid(_lowerCamelCase ) * (beta_end - beta_start) + beta_start
          else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

          A__				=   1.0 - self.betas
          A__				=   torch.cumprod(self.alphas	,      dim=0 )
          A__				=   torch.tensor(1.0 )

          A__				=   torch.tensor(1.0 )

          # standard deviation of the initial noise distribution
          A__				=   1.0

          # setable values
          A__				=   None
          A__				=   torch.from_numpy(np.arange(0	,      _lowerCamelCase )[::-1].copy() )

          A__				=   eta





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Optional[int] = None ):
          return sample





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							int	,      _lowerCamelCase :							int = 1_0	,      _lowerCamelCase :							int = 1_0	,      _lowerCamelCase :							Union[str, torch.device] = None	,      ):
          A__				=   min(self.config.num_train_timesteps	,      _lowerCamelCase )
          A__				=   num_inference_steps

          A__				=   []

          A__				=   {}
          for j in range(0	,      num_inference_steps - jump_length	,      _lowerCamelCase ):
               A__				=   jump_n_sample - 1

          A__				=   num_inference_steps
          while t >= 1:
               A__				=   t - 1
               timesteps.append(_lowerCamelCase )

               if jumps.get(_lowerCamelCase	,      0 ) > 0:
                    A__				=   jumps[t] - 1
                    for _ in range(_lowerCamelCase ):
                         A__				=   t + 1
                         timesteps.append(_lowerCamelCase )

          A__				=   np.array(_lowerCamelCase ) * (self.config.num_train_timesteps // self.num_inference_steps)
          A__				=   torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase )





     def 						A__  (					self :							int	,      _lowerCamelCase :							Dict ):
          A__				=   t - self.config.num_train_timesteps // self.num_inference_steps

          A__				=   self.alphas_cumprod[t]
          A__				=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          A__				=   1 - alpha_prod_t
          A__				=   1 - 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
          # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
          # without eta.
          # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
          A__				=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							int	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Optional[torch.Generator] = None	,      _lowerCamelCase :							bool = True	,      ):
          A__				=   timestep
          A__				=   timestep - self.config.num_train_timesteps // self.num_inference_steps

          # 1. compute alphas, betas
          A__				=   self.alphas_cumprod[t]
          A__				=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          A__				=   1 - alpha_prod_t

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          A__				=   (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               A__				=   torch.clamp(_lowerCamelCase	,      -1	,      1 )

          # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
          # substitute formula (7) in the algorithm coming from DDPM paper
          # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
          # DDIM schedule gives the same results as DDPM with eta = 1.0
          # Noise is being reused in 7. and 8., but no impact on quality has
          # been observed.

          # 5. Add noise
          A__				=   model_output.device
          A__				=   randn_tensor(model_output.shape	,      generator=_lowerCamelCase	,      device=_lowerCamelCase	,      dtype=model_output.dtype )
          A__				=   self.eta * self._get_variance(_lowerCamelCase ) ** 0.5

          A__				=   0
          if t > 0 and self.eta > 0:
               A__				=   std_dev_t * noise

          # 6. compute "direction pointing to x_t" of formula (12)
          # from https://arxiv.org/pdf/2010.02502.pdf
          A__				=   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

          # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          A__				=   alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

          # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
          A__				=   (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

          # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
          A__				=   mask * prev_known_part + (1.0 - mask) * prev_unknown_part

          if not return_dict:
               return (
                   pred_prev_sample,
                   pred_original_sample,
               )

          return RePaintSchedulerOutput(prev_sample=_lowerCamelCase	,      pred_original_sample=_lowerCamelCase )





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							Union[str, Any]=None ):
          A__				=   self.config.num_train_timesteps // self.num_inference_steps

          for i in range(_lowerCamelCase ):
               A__				=   self.betas[timestep + i]
               if sample.device.type == "mps":
                    # randn does not work reproducibly on mps
                    A__				=   randn_tensor(sample.shape	,      dtype=sample.dtype	,      generator=_lowerCamelCase )
                    A__				=   noise.to(sample.device )
               else:
                    A__				=   randn_tensor(sample.shape	,      generator=_lowerCamelCase	,      device=sample.device	,      dtype=sample.dtype )

               # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
               A__				=   (1 - beta) ** 0.5 * sample + beta**0.5 * noise

          return sample





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.IntTensor	,      ):
          raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.''' )





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








"""simple docstring"""







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =HerbertTokenizer
     _lowerCamelCase					: Optional[int]				   =HerbertTokenizerFast
     _lowerCamelCase					: Optional[Any]				   =True





     def 						A__  (					self :							Dict ):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''	,      encoding='''utf-8''' ) as f_data:
               A__				=   f_data.read().replace('''\n\n'''	,      '''\n''' ).strip()

          A__				=   [
              '''<s>''',
              '''</s>''',
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''w</w>''',
              '''r</w>''',
              '''t</w>''',
              '''lo''',
              '''low''',
              '''er</w>''',
              '''low</w>''',
              '''lowest</w>''',
              '''newer</w>''',
              '''wider</w>''',
              ''',</w>''',
              '''<unk>''',
          ]
          A__				=   dict(zip(_lowerCamelCase	,      range(len(_lowerCamelCase ) ) ) )
          A__				=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

          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''' ) as fp:
               fp.write(json.dumps(_lowerCamelCase ) )
          with open(self.merges_file	,      '''w''' ) as fp:
               fp.write('''\n'''.join(_lowerCamelCase ) )





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          A__				=   '''lower newer'''
          A__				=   '''lower newer'''
          return input_text, output_text





     def 						A__  (					self :							int ):
          A__				=   self.tokenizer_class(vocab_file=self.vocab_file	,      merges_file=self.merges_file )

          A__				=   '''lower'''
          A__				=   ['''low''', '''er</w>''']
          A__				=   tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokens + ['''<unk>''']
          A__				=   [1_6, 1_7, 2_3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )	,      _lowerCamelCase )





     def 						A__  (					self :							Optional[int] ):
          if not self.test_rust_tokenizer:
               return

          A__				=   self.get_tokenizer()
          A__				=   self.get_rust_tokenizer()

          A__				=   '''lower,newer'''

          A__				=   tokenizer.tokenize(_lowerCamelCase )
          A__				=   rust_tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   self.get_rust_tokenizer()
          A__				=   tokenizer.encode(_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )





     @slow
     def 						A__  (					self :							Any ):
          A__				=   self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased''' )

          A__				=   tokenizer.encode('''konstruowanie sekwencji'''	,      add_special_tokens=_lowerCamelCase )
          A__				=   tokenizer.encode('''konstruowanie wielu sekwencji'''	,      add_special_tokens=_lowerCamelCase )

          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,      _lowerCamelCase )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]



