






'''simple docstring'''
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{'vocab_file': 'vocab.txt'}

UpperCamelCase__ =			{
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

UpperCamelCase__ =			{
    'facebook/esm2_t6_8M_UR50D': 1024,
    'facebook/esm2_t12_35M_UR50D': 1024,
}




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   with open(_UpperCamelCase						,		"r"							) as f:
      lowercase_	:    Optional[Any]           =     f.read().splitlines()
      return [l.strip() for l in lines]





class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   List[str]		       =    VOCAB_FILES_NAMES
    __lowerCamelCase:   Any		       =    PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:   int		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:   Any		       =    ['input_ids', 'attention_mask']




    def __init__(			self  :       Tuple  ,     a  :       str  ,     a  :       List[Any]="<unk>"  ,     a  :       List[Any]="<cls>"  ,     a  :       Tuple="<pad>"  ,     a  :       int="<mask>"  ,     a  :       List[Any]="<eos>"  ,     **a  :       Dict  ,     ):






       '''simple docstring'''






       super().__init__(**a			)
       lowercase_	:    List[Any]           =     load_vocab_file(a			)
       lowercase_	:    Tuple           =     dict(enumerate(self.all_tokens			)			)
       lowercase_	:    Union[str, Any]           =     {tok: ind for ind, tok in enumerate(self.all_tokens			)}
       lowercase_	:    Any           =     unk_token
       lowercase_	:    List[Any]           =     cls_token
       lowercase_	:    str           =     pad_token
       lowercase_	:    str           =     mask_token
       lowercase_	:    List[str]           =     eos_token
       lowercase_	:    Optional[int]           =     self.all_tokens
       self._create_trie(self.unique_no_split_tokens			)




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       int			):






       '''simple docstring'''






       return self._id_to_token.get(a  ,     self.unk_token			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       str			):






       '''simple docstring'''






       return self._token_to_id.get(a  ,     self._token_to_id.get(self.unk_token			)			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[Any]  ,     **a  :       Union[str, Any]			):






       '''simple docstring'''






       return text.split()




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       List[str]=False			):






       '''simple docstring'''






       return len(self._id_to_token			)




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return {token: i for i, token in enumerate(self.all_tokens			)}




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       str			):






       '''simple docstring'''






       return self._token_to_id.get(a  ,     self._token_to_id.get(self.unk_token			)			)




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int			):






       '''simple docstring'''






       return self._id_to_token.get(a  ,     self.unk_token			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       List[int]  ,     a  :       Optional[List[int]] = None			):






       '''simple docstring'''






       lowercase_	:    int           =     [self.cls_token_id]
       lowercase_	:    Any           =     [self.eos_token_id]  # No sep token in ESM vocabulary
       if token_ids_a is None:
          if self.eos_token_id is None:
             return cls + token_ids_a
          else:
             return cls + token_ids_a + sep
       elif self.eos_token_id is None:
          raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"			)
       return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       List  ,     a  :       Optional[List] = None  ,     a  :       bool = False			):






       '''simple docstring'''






       if already_has_special_tokens:
          if token_ids_a is not None:
             raise ValueError(
                 "You should not supply a second sequence if the provided sequence of "
                 "ids is already formatted with special tokens for the model."			)

          return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
       lowercase_	:    Dict           =     [1] + ([0] * len(a			)) + [1]
       if token_ids_a is not None:
          mask += [0] * len(a			) + [1]
       return mask




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Union[str, Any]  ,     a  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     os.path.join(a  ,     (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"			)
       with open(a  ,     "w"			) as f:
          f.write("\n".join(self.all_tokens			)			)
       return (vocab_file,)




    @property
    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       return self.get_vocab_size(with_added_tokens=a			)





    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[List[str], List[AddedToken]]  ,     a  :       bool = False			):






       '''simple docstring'''






       return super()._add_tokens(a  ,     special_tokens=a			)












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




