
'''simple docstring'''

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

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


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)


__snake_case:  Dict								=			{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__snake_case:  int								=			{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__snake_case:  Optional[Any]								=			'</w>'
__snake_case:  Optional[Any]								=			'@@ '






def 	_snake_case						(      A_       :    int   ):
		"""simple docstring"""







		a_      :	List[str]      =							set()
		a_      :	Any      =							word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)   )
				a_      :	List[str]      =							char
		return pairs


# Speech2Text2 has no max input length
__snake_case:  Dict								=			{'facebook/s2t-wav2vec2-large-en-de': 10_24}




class  _UpperCAmelCase ( lowerCamelCase__    ):

					"""simple docstring"""

					a_    	=					VOCAB_FILES_NAMES
					a_    	=					PRETRAINED_VOCAB_FILES_MAP
					a_    	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_    	=					["input_ids", "attention_mask"]


					def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_="<s>"							,		lowerCAmelCase_="<pad>"							,		lowerCAmelCase_="</s>"							,		lowerCAmelCase_="<unk>"							,		lowerCAmelCase_=False							,		lowerCAmelCase_=None							,		**lowerCAmelCase_							,		):

							'''simple docstring'''

							super().__init__(
							    unk_token=__lowerCamelCase							,		bos_token=__lowerCamelCase							,		eos_token=__lowerCamelCase							,		pad_token=__lowerCamelCase							,		do_lower_case=__lowerCamelCase							,		**__lowerCamelCase							,		)

							a_      :	int      =							do_lower_case

							with open(__lowerCamelCase							,		encoding="""utf-8""" ) as vocab_handle:
									a_      :	List[Any]      =							json.load(__lowerCamelCase )
							a_      :	Dict      =							{v: k for k, v in self.encoder.items()}

							if merges_file is None:
									logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )

									a_      :	Any      =							None
									a_      :	Tuple      =							None
							else:
									with open(__lowerCamelCase							,		encoding="""utf-8""" ) as merges_handle:
											a_      :	List[Any]      =							merges_handle.read().split("""\n""" )[:-1]

									a_      :	int      =							[tuple(merge.split()[:2] ) for merge in merges]
									a_      :	str      =							dict(zip(__lowerCamelCase							,		range(len(__lowerCamelCase ) ) ) )
									a_      :	Optional[Any]      =							{}


					@property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return len(self.decoder )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return dict(self.encoder							,		**self.added_tokens_encoder )


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	Any      =							tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
									return self.cache[token]
							a_      :	Any      =							get_pairs(__lowerCamelCase )

							if not pairs:
									return token

							while True:
									a_      :	Optional[int]      =							min(__lowerCamelCase							,		key=lambda lowerCAmelCase_ : self.bpe_ranks.get(__lowerCamelCase							,		float("""inf""" ) ) )
									if bigram not in self.bpe_ranks:
											break
									a_      :	str      =							bigram
									a_      :	Union[str, Any]      =							[]
									a_      :	Any      =							0
									while i < len(__lowerCamelCase ):
											try:
													a_      :	List[Any]      =							word.index(__lowerCamelCase							,		__lowerCamelCase )
											except ValueError:
													new_word.extend(word[i:] )
													break
											else:
													new_word.extend(word[i:j] )
													a_      :	Tuple      =							j

											if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
													new_word.append(first + second )
													i += 2
											else:
													new_word.append(word[i] )
													i += 1
									a_      :	int      =							tuple(__lowerCamelCase )
									a_      :	Union[str, Any]      =							new_word
									if len(__lowerCamelCase ) == 1:
											break
									else:
											a_      :	List[str]      =							get_pairs(__lowerCamelCase )
							a_      :	int      =							''' '''.join(__lowerCamelCase )
							if word == "\n  " + BPE_TOKEN_MERGES:
									a_      :	str      =							'''\n''' + BPE_TOKEN_MERGES

							if word.endswith(__lowerCamelCase ):
									a_      :	Optional[int]      =							word.replace(__lowerCamelCase							,		"""""" )

							a_      :	int      =							word.replace(""" """							,		__lowerCamelCase )
							a_      :	Any      =							word
							return word


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

							if self.bpe_ranks is None:
									raise ValueError(
									    """This tokenizer was instantiated without a `merges.txt` file, so"""
									    """ that it can only be used for decoding, not for encoding."""
									    """Make sure to provide `merges.txt` file at instantiation to enable """
									    """encoding.""" )

							if self.do_lower_case:
									a_      :	Union[str, Any]      =							text.lower()

							a_      :	Union[str, Any]      =							text.split()

							a_      :	List[Any]      =							[]
							for token in text:
									if token:
											split_tokens.extend(list(self.bpe(__lowerCamelCase ).split(""" """ ) ) )

							return split_tokens


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

							return self.encoder.get(__lowerCamelCase							,		self.encoder.get(self.unk_token ) )


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	Any      =							self.decoder.get(__lowerCamelCase							,		self.unk_token )
							return result


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	int      =							''' '''.join(__lowerCamelCase )

							# make sure @@ tokens are concatenated
							a_      :	int      =							''''''.join(string.split(__lowerCamelCase ) )

							return string






					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

							'''simple docstring'''

							if not os.path.isdir(__lowerCamelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
							a_      :	Tuple      =							os.path.join(
							    __lowerCamelCase							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
							a_      :	Optional[int]      =							os.path.join(
							    __lowerCamelCase							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

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

							a_      :	Optional[int]      =							0
							if self.bpe_ranks is None:
									return (vocab_file,)

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

							return (vocab_file, merges_file)




'''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 ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					None







def 	_snake_case						(      A_       :    int     ,		A_       :    List[str]=0.999     ,		A_       :    List[Any]="cosine"     ,		):
  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_       :    List[Any]   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_       :    List[str]   ):
      return math.exp(t * -12.0   )

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

  a_      :	int      =							[]
  for i in range(A_   ):
    a_      :	Optional[Any]      =							i / num_diffusion_timesteps
    a_      :	List[Any]      =							(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 ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = "fixed_small_log"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "squaredcos_cap_v2"							,		):

       '''simple docstring'''

       if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )

       a_      :	Optional[Any]      =							betas_for_alpha_bar(lowerCAmelCase_ )

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

       # standard deviation of the initial noise distribution
       a_      :	Any      =							1.0

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

       a_      :	Any      =							variance_type


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[str]      =							num_inference_steps
       a_      :	Dict      =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a_      :	Optional[Any]      =							(np.arange(0							,		lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       a_      :	List[Any]      =							torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if prev_timestep is None:
         a_      :	Tuple      =							t - 1

       a_      :	Optional[Any]      =							self.alphas_cumprod[t]
       a_      :	Union[str, Any]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Optional[Any]      =							1 - alpha_prod_t
       a_      :	str      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	int      =							self.betas[t]
       else:
         a_      :	Tuple      =							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_      :	str      =							beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
         a_      :	int      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
         a_      :	List[Any]      =							torch.log(torch.clamp(lowerCAmelCase_							,		min=1E-2_0 ) )
         a_      :	List[Any]      =							torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a_      :	Tuple      =							variance.log()
         a_      :	int      =							beta.log()

         a_      :	List[Any]      =							(predicted_variance + 1) / 2
         a_      :	Dict      =							frac * max_log + (1 - frac) * min_log

       return variance


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Tuple      =							timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         a_      , a_      :	Optional[Any]      =							torch.split(lowerCAmelCase_							,		sample.shape[1]							,		dim=1 )
       else:
         a_      :	Optional[int]      =							None

       # 1. compute alphas, betas
       if prev_timestep is None:
         a_      :	List[Any]      =							t - 1

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

       if prev_timestep == t - 1:
         a_      :	Optional[int]      =							self.betas[t]
         a_      :	Any      =							self.alphas[t]
       else:
         a_      :	List[Any]      =							1 - alpha_prod_t / alpha_prod_t_prev
         a_      :	List[str]      =							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_      :	List[str]      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
         a_      :	Optional[int]      =							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_      :	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
       a_      :	str      =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       a_      :	Dict      =							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_      :	Any      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

         a_      :	List[Any]      =							self._get_variance(
             lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		)

         if self.variance_type == "fixed_small_log":
           a_      :	Dict      =							variance
         elif self.variance_type == "learned_range":
           a_      :	Any      =							(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_      :	List[str]      =							variance * variance_noise

       a_      :	Optional[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_							,		):

       '''simple docstring'''

       a_      :	List[str]      =							self.alphas_cumprod.to(device=original_samples.device							,		dtype=original_samples.dtype )
       a_      :	Union[str, Any]      =							timesteps.to(original_samples.device )

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

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

       a_      :	int      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples


