
"""simple docstring"""


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

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


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowercase											=			{
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

_lowercase											=			{
    "microsoft/biogpt": 10_24,
}






def  _snake_case			(						snake_case__				:       List[Any]       ):
 A  				=				set()
 A  				=				word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)       )
  A  				=				char
 return pairs







class 			lowerCAmelCase_			( UpperCAmelCase__      ):



      '''simple docstring'''


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


      def __init__(	self       : Union[str, Any]					,A_       : Dict					,A_       : Tuple					,A_       : List[Any]="<unk>"					,A_       : Tuple="<s>"					,A_       : Tuple="</s>"					,A_       : Union[str, Any]="</s>"					,A_       : str="<pad>"					,**A_       : List[Any]					,)       ->				str:
       super().__init__(
           bos_token=__lowercase					,eos_token=__lowercase					,sep_token=__lowercase					,unk_token=__lowercase					,pad_token=__lowercase					,**__lowercase					,)

       try:
        import sacremoses
       except ImportError:
        raise ImportError(
            'You need to install sacremoses to use BioGptTokenizer. '
            'See https://pypi.org/project/sacremoses/ for installation.' )

       A  				=				'en'
       A  				=				sacremoses
       # cache of sm.MosesTokenizer instance
       A  				=				{}
       A  				=				{}
       with open(__lowercase					,encoding='utf-8' ) as vocab_handle:
        A  				=				json.load(__lowercase )
       A  				=				{v: k for k, v in self.encoder.items()}
       with open(__lowercase					,encoding='utf-8' ) as merges_handle:
        A  				=				merges_handle.read().split('\n' )[:-1]
       A  				=				[tuple(merge.split()[:2] ) for merge in merges]
       A  				=				dict(zip(__lowercase					,range(len(__lowercase ) ) ) )
       A  				=				{}


      @property
      def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Any:
       return len(self.encoder )


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Any:
       return dict(self.encoder					,**self.added_tokens_encoder )


      def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Dict					,A_       : int )       ->				Union[str, Any]:






       if lang not in self.cache_moses_tokenizer:
        A  				=				self.sm.MosesTokenizer(lang=__lowercase )
        A  				=				moses_tokenizer
       return self.cache_moses_tokenizer[lang].tokenize(
           __lowercase					,aggressive_dash_splits=__lowercase					,return_str=__lowercase					,escape=__lowercase )


      def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : str					,A_       : List[str] )       ->				Any:






       if lang not in self.cache_moses_detokenizer:
        A  				=				self.sm.MosesDetokenizer(lang=__lowercase )
        A  				=				moses_detokenizer
       return self.cache_moses_detokenizer[lang].detokenize(__lowercase )


      def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Dict )       ->				int:
       A  				=				tuple(token[:-1] ) + (token[-1] + '</w>',)
       if token in self.cache:
        return self.cache[token]
       A  				=				get_pairs(__lowercase )

       if not pairs:
        return token + "</w>"

       while True:
        A  				=				min(__lowercase					,key=lambda A_ : self.bpe_ranks.get(__lowercase					,float('inf' ) ) )
        if bigram not in self.bpe_ranks:
         break
        A				, A  				=				bigram
        A  				=				[]
        A  				=				0
        while i < len(__lowercase ):
         try:
          A  				=				word.index(__lowercase					,__lowercase )
         except ValueError:
          new_word.extend(word[i:] )
          break
         else:
          new_word.extend(word[i:j] )
          A  				=				j

         if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
          new_word.append(first + second )
          i += 2
         else:
          new_word.append(word[i] )
          i += 1
        A  				=				tuple(__lowercase )
        A  				=				new_word
        if len(__lowercase ) == 1:
         break
        else:
         A  				=				get_pairs(__lowercase )
       A  				=				' '.join(__lowercase )
       if word == "\n  </w>":
        A  				=				'\n</w>'
       A  				=				word
       return word


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : int					,A_       : Dict=False )       ->				Dict:






       if bypass_tokenizer:
        A  				=				text.split()
       else:
        A  				=				self.moses_tokenize(__lowercase					,self.lang )

       A  				=				[]
       for token in text:
        if token:
         split_tokens.extend(list(self.bpe(__lowercase ).split(' ' ) ) )

       return split_tokens


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : List[str] )       ->				Optional[int]:
       return self.encoder.get(__lowercase					,self.encoder.get(self.unk_token ) )


      def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : List[str] )       ->				List[Any]:
       return self.decoder.get(__lowercase					,self.unk_token )


      def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : str )       ->				str:
       A  				=				[t.replace(' '					,'' ).replace('</w>'					,' ' ) for t in tokens]
       A  				=				''.join(__lowercase ).split()
       # detokenize
       A  				=				self.moses_detokenize(__lowercase					,self.lang )
       return text


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : List[int]					,A_       : Optional[List[int]] = None )       ->				List[Any]:






       if token_ids_a is None:
        return [self.sep_token_id] + token_ids_a
       A  				=				[self.sep_token_id]
       return sep + token_ids_a + sep + token_ids_a


      def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : List[int]					,A_       : Optional[List[int]] = None					,A_       : bool = False )       ->				str:






       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__lowercase					,token_ids_a=__lowercase					,already_has_special_tokens=__lowercase )
       # no bos used in fairseq
       if token_ids_a is not None:
        return [1] + ([0] * len(__lowercase )) + [1] + ([0] * len(__lowercase ))
       return [1] + ([0] * len(__lowercase ))


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : List[int]					,A_       : Optional[List[int]] = None )       ->				Union[str, Any]:
       A  				=				[self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
        return len(token_ids_a + sep ) * [0]
       return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]


      def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : str					,A_       : Optional[str] = None )       ->				Optional[int]:






       if not os.path.isdir(__lowercase ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
        return
       A  				=				os.path.join(
           __lowercase					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
       A  				=				os.path.join(
           __lowercase					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

       A  				=				0
       with open(__lowercase					,'w'					,encoding='utf-8' ) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,key=lambda A_ : 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(__lowercase ) + '\n' )
         index += 1

       return vocab_file, merge_file


      def __getstate__(	self       : Tuple )       ->				int:
       A  				=				self.__dict__.copy()
       A  				=				None
       return state




      def __setstate__(	self       : List[str]					,A_       : List[str] )       ->				Tuple:
       A  				=				d

       try:
        import sacremoses
       except ImportError:
        raise ImportError(
            'You need to install sacremoses to use XLMTokenizer. '
            'See https://pypi.org/project/sacremoses/ for installation.' )

       A  				=				sacremoses

"""simple docstring"""


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

import numpy as np
import torch

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


_lowercase											=			logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

							A  				=				np.linspace(0					,1					,A_ )
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							# setable values
							A  				=				None
							A  				=				torch.from_numpy(A_ )
							A  				=				torch.from_numpy(A_ )
							A  				=				False
							A  				=				False


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

							A  				=				(schedule_timesteps == timestep).nonzero()
							return indices.item()


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

							A  				=				sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							if num_inference_steps is None and timesteps is None:
								raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.' )

							if num_inference_steps is not None and timesteps is not None:
								raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.' )

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
								for i in range(1					,len(A_ ) ):
									if timesteps[i] >= timesteps[i - 1]:
										raise ValueError('`timesteps` must be in descending order.' )

								if timesteps[0] >= self.config.num_train_timesteps:
									raise ValueError(
									    F'`timesteps` must start before `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps}.' )

								A  				=				np.array(A_					,dtype=np.intaa )
								A  				=				True
							else:
								if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
									    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
									    F' maximal {self.config.num_train_timesteps} timesteps.' )

								A  				=				num_inference_steps

								A  				=				self.config.num_train_timesteps // self.num_inference_steps
								A  				=				(np.arange(0					,A_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								A  				=				False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							A  				=				self.config.num_train_timesteps
							A  				=				timesteps[::-1].copy()
							A  				=				ramp / (num_train_timesteps - 1)
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							A  				=				np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
							A  				=				torch.from_numpy(A_ ).to(device=A_ )

							if str(A_ ).startswith('mps' ):
								# mps does not support float64
								A  				=				torch.from_numpy(A_ ).to(A_					,dtype=torch.floataa )
							else:
								A  				=				torch.from_numpy(A_ ).to(device=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Any:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.config.rho
							A  				=				sigma_min ** (1 / rho)
							A  				=				sigma_max ** (1 / rho)
							A  				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / (sigma**2 + sigma_data**2)
							A  				=				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							A  				=				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,torch.LongTensor )
							):
								raise ValueError(
								    (
								        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
								        F' `{self.__class__}.step()` is not supported. Make sure to pass'
								        ' one of the `scheduler.timesteps` as a timestep.'
								    )					,)

							if not self.is_scale_input_called:
								logger.warning(
								    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
								    'See `StableDiffusionPipeline` for a usage example.' )

							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.index_for_timestep(A_ )

							# sigma_next corresponds to next_t in original implementation
							A  				=				self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
								A  				=				self.sigmas[step_index + 1]
							else:
								# Set sigma_next to sigma_min
								A  				=				self.sigmas[-1]

							# Get scalings for boundary conditions
							A				, A  				=				self.get_scalings_for_boundary_condition(A_ )

							# 1. Denoise model output using boundary conditions
							A  				=				c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
								A  				=				denoised.clamp(-1					,1 )

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps ) > 1:
								A  				=				randn_tensor(
								    model_output.shape					,dtype=model_output.dtype					,device=model_output.device					,generator=A_ )
							else:
								A  				=				torch.zeros_like(A_ )
							A  				=				noise * self.config.s_noise

							A  				=				sigma_next.clamp(min=A_					,max=A_ )

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							A  				=				denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
								return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,)       ->				torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							A  				=				self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
								# mps does not support float64
								A  				=				self.timesteps.to(original_samples.device					,dtype=torch.floataa )
								A  				=				timesteps.to(original_samples.device					,dtype=torch.floataa )
							else:
								A  				=				self.timesteps.to(original_samples.device )
								A  				=				timesteps.to(original_samples.device )

							A  				=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							A  				=				sigmas[step_indices].flatten()
							while len(sigma.shape ) < len(original_samples.shape ):
								A  				=				sigma.unsqueeze(-1 )

							A  				=				original_samples + noise * sigma
							return noisy_samples




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