



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			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    torch.FloatTensor
 _lowercase   :    Optional[torch.FloatTensor]      						=					None






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase=0.999							,  __UpperCAmelCase="cosine"							,  ):
 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__												= []
 for i in range(__UpperCAmelCase       ):
  a__												= i / num_diffusion_timesteps
  a__												= (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			(       _lowercase	,	_lowercase				):

 """simple docstring"""



 @register_to_config
 def __init__(	self		,				SCREAMING_SNAKE_CASE = 1_0_0_0		,				SCREAMING_SNAKE_CASE = "fixed_small_log"		,				SCREAMING_SNAKE_CASE = True		,				SCREAMING_SNAKE_CASE = 1.0		,				SCREAMING_SNAKE_CASE = "epsilon"		,				SCREAMING_SNAKE_CASE = "squaredcos_cap_v2"		,				) ->			Tuple:
  if beta_schedule != "squaredcos_cap_v2":
   raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''      )

  a__												= betas_for_alpha_bar(SCREAMING_SNAKE_CASE      )

  a__												= 1.0 - self.betas
  a__												= torch.cumprod(self.alphas		,				dim=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		,				SCREAMING_SNAKE_CASE      )[::-1].copy()      )

  a__												= variance_type

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			torch.FloatTensor:
  return sample

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			Union[str, Any]:
  a__												= num_inference_steps
  a__												= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
  a__												= (np.arange(0		,				SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
  a__												= torch.from_numpy(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None      ) ->			Optional[Any]:
  if prev_timestep is None:
   a__												= t - 1

  a__												= self.alphas_cumprod[t]
  a__												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
  a__												= 1 - alpha_prod_t
  a__												= 1 - alpha_prod_t_prev

  if prev_timestep == t - 1:
   a__												= self.betas[t]
  else:
   a__												= 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
  a__												= beta_prod_t_prev / beta_prod_t * beta

  if variance_type is None:
   a__												= self.config.variance_type

  # hacks - were probably added for training stability
  if variance_type == "fixed_small_log":
   a__												= torch.log(torch.clamp(SCREAMING_SNAKE_CASE		,				min=1e-2_0      )      )
   a__												= torch.exp(0.5 * variance      )
  elif variance_type == "learned_range":
   # NOTE difference with DDPM scheduler
   a__												= variance.log()
   a__												= beta.log()

   a__												= (predicted_variance + 1) / 2
   a__												= frac * max_log + (1 - frac) * min_log

  return variance

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE = True		,				) ->			Union[UnCLIPSchedulerOutput, Tuple]:
  a__												= timestep

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

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

  a__												= self.alphas_cumprod[t]
  a__												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
  a__												= 1 - alpha_prod_t
  a__												= 1 - alpha_prod_t_prev

  if prev_timestep == t - 1:
   a__												= self.betas[t]
   a__												= self.alphas[t]
  else:
   a__												= 1 - alpha_prod_t / alpha_prod_t_prev
   a__												= 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":
   a__												= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif self.config.prediction_type == "sample":
   a__												= 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:
   a__												= torch.clamp(
       SCREAMING_SNAKE_CASE		,				-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
  a__												= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
  a__												= 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
  a__												= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  a__												= 0
  if t > 0:
   a__												= randn_tensor(
       model_output.shape		,				dtype=model_output.dtype		,				generator=SCREAMING_SNAKE_CASE		,				device=model_output.device      )

   a__												= self._get_variance(
       SCREAMING_SNAKE_CASE		,				predicted_variance=SCREAMING_SNAKE_CASE		,				prev_timestep=SCREAMING_SNAKE_CASE		,				)

   if self.variance_type == "fixed_small_log":
    a__												= variance
   elif self.variance_type == "learned_range":
    a__												= (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.'''      )

   a__												= variance * variance_noise

  a__												= pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample,)

  return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE		,				pred_original_sample=SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				) ->			torch.FloatTensor:
  # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
  a__												= self.alphas_cumprod.to(device=original_samples.device		,				dtype=original_samples.dtype      )
  a__												= timesteps.to(original_samples.device      )

  a__												= alphas_cumprod[timesteps] ** 0.5
  a__												= sqrt_alpha_prod.flatten()
  while len(sqrt_alpha_prod.shape      ) < len(original_samples.shape      ):
   a__												= sqrt_alpha_prod.unsqueeze(-1      )

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

  a__												= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples










import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_      :      Dict    =     logging.get_logger(__name__)


a_      :      Any    =     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_      :      Union[str, Any]    =     {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

a_      :      List[Any]    =     {'facebook/blenderbot_small-90M': 5_12}






def     __a					(		__UpperCAmelCase       ):
 a__												= set()
 a__												= word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)       )
  a__												= char

 a__												= set(__UpperCAmelCase       )
 return pairs

class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Union[str, Any]      						=					VOCAB_FILES_NAMES
 _lowercase   :    List[Any]      						=					PRETRAINED_VOCAB_FILES_MAP
 _lowercase   :    Tuple      						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase   :    int      						=					['''input_ids''', '''attention_mask''']

 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE="__start__"		,				SCREAMING_SNAKE_CASE="__end__"		,				SCREAMING_SNAKE_CASE="__unk__"		,				SCREAMING_SNAKE_CASE="__null__"		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(unk_token=SCREAMING_SNAKE_CASE		,				bos_token=SCREAMING_SNAKE_CASE		,				eos_token=SCREAMING_SNAKE_CASE		,				pad_token=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  with open(SCREAMING_SNAKE_CASE		,				encoding='''utf-8'''      ) as vocab_handle:
   a__												= json.load(SCREAMING_SNAKE_CASE      )
  a__												= {v: k for k, v in self.encoder.items()}
  with open(SCREAMING_SNAKE_CASE		,				encoding='''utf-8'''      ) as merges_handle:
   a__												= merges_handle.read().split('''\n'''      )[1:-1]
  a__												= [tuple(merge.split()      ) for merge in merges]
  a__												= dict(zip(SCREAMING_SNAKE_CASE		,				range(len(SCREAMING_SNAKE_CASE      )      )      )      )
  a__												= {}

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return len(self.encoder      )

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  return dict(self.encoder		,				**self.added_tokens_encoder      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  if token in self.cache:
   return self.cache[token]
  a__												= re.sub('''([.,!?()])'''		,				R''' \1'''		,				SCREAMING_SNAKE_CASE      )
  a__												= re.sub('''(\')'''		,				R''' \1 '''		,				SCREAMING_SNAKE_CASE      )
  a__												= re.sub(R'''\s{2,}'''		,				''' '''		,				SCREAMING_SNAKE_CASE      )
  if "\n" in token:
   a__												= token.replace('''\n'''		,				''' __newln__'''      )

  a__												= token.split(''' '''      )
  a__												= []
  for token in tokens:
   if not len(SCREAMING_SNAKE_CASE      ):
    continue

   a__												= token.lower()
   a__												= tuple(SCREAMING_SNAKE_CASE      )
   a__												= tuple(list(word[:-1]      ) + [word[-1] + '''</w>''']      )
   a__												= get_pairs(SCREAMING_SNAKE_CASE      )

   if not pairs:
    words.append(SCREAMING_SNAKE_CASE      )
    continue

   while True:
    a__												= min(SCREAMING_SNAKE_CASE		,				key=lambda SCREAMING_SNAKE_CASE      : self.bpe_ranks.get(SCREAMING_SNAKE_CASE		,				float('''inf'''      )      )      )
    if bigram not in self.bpe_ranks:
     break
    a__							,			a__												= bigram
    a__												= []
    a__												= 0

    while i < len(SCREAMING_SNAKE_CASE      ):
     try:
      a__												= word.index(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      new_word.extend(word[i:j]      )
      a__												= j
     except ValueError:
      new_word.extend(word[i:]      )
      break

     if word[i] == first and i < len(SCREAMING_SNAKE_CASE      ) - 1 and word[i + 1] == second:
      new_word.append(first + second      )
      i += 2
     else:
      new_word.append(word[i]      )
      i += 1
    a__												= tuple(SCREAMING_SNAKE_CASE      )
    a__												= new_word
    if len(SCREAMING_SNAKE_CASE      ) == 1:
     break
    else:
     a__												= get_pairs(SCREAMING_SNAKE_CASE      )
   a__												= '''@@ '''.join(SCREAMING_SNAKE_CASE      )
   a__												= word[:-4]

   a__												= word
   words.append(SCREAMING_SNAKE_CASE      )
  return " ".join(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= []

  a__												= re.findall(R'''\S+\n?'''		,				SCREAMING_SNAKE_CASE      )

  for token in words:
   split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE      ).split(''' '''      )      )      )
  return split_tokens

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= token.lower()
  return self.encoder.get(SCREAMING_SNAKE_CASE		,				self.encoder.get(self.unk_token      )      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  return self.decoder.get(SCREAMING_SNAKE_CASE		,				self.unk_token      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  a__												= ''' '''.join(SCREAMING_SNAKE_CASE      ).replace('''@@ '''		,				''''''      ).strip()
  return out_string



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			Tuple[str]:
  if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
   return
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

  with open(SCREAMING_SNAKE_CASE		,				'''w'''		,				encoding='''utf-8'''      ) as f:
   f.write(json.dumps(self.encoder		,				indent=2		,				sort_keys=SCREAMING_SNAKE_CASE		,				ensure_ascii=SCREAMING_SNAKE_CASE      ) + '''\n'''      )

  a__												= 0
  with open(SCREAMING_SNAKE_CASE		,				'''w'''		,				encoding='''utf-8'''      ) as writer:
   writer.write('''#version: 0.2\n'''      )
   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,				key=lambda SCREAMING_SNAKE_CASE      : kv[1]      ):
    if index != token_index:
     logger.warning(
         f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
         ''' Please check that the tokenizer is not corrupted!'''      )
     a__												= token_index
    writer.write(''' '''.join(SCREAMING_SNAKE_CASE      ) + '''\n'''      )
    index += 1

  return vocab_file, merge_file





