






"""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    (			snake_case_   ):
 UpperCamelCase      : torch.FloatTensor
 UpperCamelCase      : torch.FloatTensor







def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=0.999  ,     UpperCamelCase__="cosine"  ,     ):



  '''simple docstring'''
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCamelCase__       ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

  elif alpha_transform_type == "exp":

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

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

  _a						: Any    =			[]
  for i in range(UpperCamelCase__       ):
    _a						: List[Any]    =			i / num_diffusion_timesteps
    _a						: Optional[int]    =			(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    (			snake_case_	,  snake_case_   ):
 UpperCamelCase      : Optional[Any]    =      1


 @register_to_config
 def __init__(  self	:		List[str]							,		UpperCAmelCase__	:		int = 1000							,		UpperCAmelCase__	:		float = 0.0_0_0_1							,		UpperCAmelCase__	:		float = 0.0_2							,		UpperCAmelCase__	:		str = "linear"							,		UpperCAmelCase__	:		float = 0.0							,		UpperCAmelCase__	:		Optional[np.ndarray] = None							,		UpperCAmelCase__	:		bool = True							,		)      -> Optional[Any]:
   if trained_betas is not None:
     _a						: Dict    =			torch.from_numpy(UpperCAmelCase__							)
   elif beta_schedule == "linear":
     _a						: Dict    =			torch.linspace(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		dtype=torch.floataa							)
   elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     _a						: Dict    =			(
         torch.linspace(beta_start**0.5							,		beta_end**0.5							,		UpperCAmelCase__							,		dtype=torch.floataa							) ** 2
     )
   elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     _a						: int    =			betas_for_alpha_bar(UpperCAmelCase__							)
   elif beta_schedule == "sigmoid":
     # GeoDiff sigmoid schedule
     _a						: Optional[int]    =			torch.linspace(-6							,		6							,		UpperCAmelCase__							)
     _a						: Union[str, Any]    =			torch.sigmoid(UpperCAmelCase__							) * (beta_end - beta_start) + beta_start
   else:
     raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

   _a						: Optional[int]    =			1.0 - self.betas
   _a						: Optional[int]    =			torch.cumprod(self.alphas							,		dim=0							)
   _a						: Tuple    =			torch.tensor(1.0							)

   _a						: Any    =			torch.tensor(1.0							)

   # standard deviation of the initial noise distribution
   _a						: Optional[int]    =			1.0

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

   _a						: Union[str, Any]    =			eta


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		Optional[int] = None							)      -> torch.FloatTensor:
   return sample


 def _lowercase			(  self	:		Optional[Any]							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		int = 10							,		UpperCAmelCase__	:		int = 10							,		UpperCAmelCase__	:		Union[str, torch.device] = None							,		)      -> Dict:
   _a						: Tuple    =			min(self.config.num_train_timesteps							,		UpperCAmelCase__							)
   _a						: List[Any]    =			num_inference_steps

   _a						: Optional[int]    =			[]

   _a						: Tuple    =			{}
   for j in range(0							,		num_inference_steps - jump_length							,		UpperCAmelCase__							):
     _a						: Dict    =			jump_n_sample - 1

   _a						: Tuple    =			num_inference_steps
   while t >= 1:
     _a						: Dict    =			t - 1
     timesteps.append(UpperCAmelCase__							)

     if jumps.get(UpperCAmelCase__							,		0							) > 0:
       _a						: Union[str, Any]    =			jumps[t] - 1
       for _ in range(UpperCAmelCase__							):
         _a						: Optional[Any]    =			t + 1
         timesteps.append(UpperCAmelCase__							)

   _a						: List[str]    =			np.array(UpperCAmelCase__							) * (self.config.num_train_timesteps // self.num_inference_steps)
   _a						: int    =			torch.from_numpy(UpperCAmelCase__							).to(UpperCAmelCase__							)


 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		List[Any]							)      -> Any:
   _a						: str    =			t - self.config.num_train_timesteps // self.num_inference_steps

   _a						: Optional[Any]    =			self.alphas_cumprod[t]
   _a						: Tuple    =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
   _a						: List[str]    =			1 - alpha_prod_t
   _a						: List[Any]    =			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						: str    =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

   return variance


 def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		Optional[torch.Generator] = None							,		UpperCAmelCase__	:		bool = True							,		)      -> Union[RePaintSchedulerOutput, Tuple]:
   _a						: Dict    =			timestep
   _a						: int    =			timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 1. compute alphas, betas
   _a						: Dict    =			self.alphas_cumprod[t]
   _a						: List[Any]    =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
   _a						: List[str]    =			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						: List[str]    =			(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     _a						: List[Any]    =			torch.clamp(UpperCAmelCase__							,		-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						: Any    =			model_output.device
   _a						: str    =			randn_tensor(model_output.shape							,		generator=UpperCAmelCase__							,		device=UpperCAmelCase__							,		dtype=model_output.dtype							)
   _a						: List[Any]    =			self.eta * self._get_variance(UpperCAmelCase__							) ** 0.5

   _a						: int    =			0
   if t > 0 and self.eta > 0:
     _a						: Any    =			std_dev_t * noise

   # 6. compute "direction pointing to x_t" of formula (12)
   # from https://arxiv.org/pdf/2010.02502.pdf
   _a						: str    =			(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						: List[str]    =			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						: List[str]    =			(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						: str    =			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=UpperCAmelCase__							,		pred_original_sample=UpperCAmelCase__							)


 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		Any=None							)      -> Optional[int]:
   _a						: Any    =			self.config.num_train_timesteps // self.num_inference_steps

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

     # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
     _a						: Optional[int]    =			(1 - beta) ** 0.5 * sample + beta**0.5 * noise

   return sample


 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		torch.IntTensor							,		)      -> torch.FloatTensor:
   raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""							)







 def __len__(  self	:		Optional[Any]							)      -> Any:
   return self.config.num_train_timesteps









"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


 def _lowercase			(  self	:		Dict							)      -> List[Any]:
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

