






"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				CommonSchedulerState

					# setable values
					SCREAMING_SNAKE_CASE_		:				jnp.ndarray
					SCREAMING_SNAKE_CASE_		:				jnp.ndarray
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   None




					@classmethod
					def   A  (				cls     :						int    ,						A     :						CommonSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray   )  ->  Optional[Any]:
							return cls(common=A    ,						init_noise_sigma=A    ,						timesteps=A   )





@dataclass
class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				DDPMSchedulerState





class 					_UpperCAmelCase       ( _A   ,						_A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   [e.name for e in FlaxKarrasDiffusionSchedulers]

					SCREAMING_SNAKE_CASE_		:				jnp.dtype




					@property
					def   A  (				self     :						List[str]   )  ->  Any:
							return True




					@register_to_config
					def __init__(				self     :						str    ,						A     :						int = 10_00    ,						A     :						float = 0.0001    ,						A     :						float = 0.02    ,						A     :						str = "linear"    ,						A     :						Optional[jnp.ndarray] = None    ,						A     :						str = "fixed_small"    ,						A     :						bool = True    ,						A     :						str = "epsilon"    ,						A     :						jnp.dtype = jnp.floataa    ,						)  ->  Any:
							lowercase_ : List[str]							       =   dtype




					def   A  (				self     :						int    ,						A     :						Optional[CommonSchedulerState] = None   )  ->  DDPMSchedulerState:
							if common is None:
									lowercase_ : str							       =   CommonSchedulerState.create(self   )

							# standard deviation of the initial noise distribution
							lowercase_ : Tuple							       =   jnp.array(1.0    ,						dtype=self.dtype   )

							lowercase_ : List[str]							       =   jnp.arange(0    ,						self.config.num_train_timesteps   ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=A    ,						init_noise_sigma=A    ,						timesteps=A    ,						)




					def   A  (				self     :						List[str]    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						Optional[int] = None   )  ->  jnp.ndarray:
							return sample




					def   A  (				self     :						Dict    ,						A     :						DDPMSchedulerState    ,						A     :						int    ,						A     :						Tuple = ()   )  ->  DDPMSchedulerState:
							lowercase_ : Optional[int]							       =   self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							lowercase_ : Tuple							       =   (jnp.arange(0    ,						A   ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=A    ,						timesteps=A    ,						)




					def   A  (				self     :						int    ,						A     :						DDPMSchedulerState    ,						A     :						Any    ,						A     :						Optional[int]=None    ,						A     :						Any=None   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   state.common.alphas_cumprod[t]
							lowercase_ : int							       =   jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )

							# 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
							lowercase_ : int							       =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
									lowercase_ : str							       =   self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
									lowercase_ : Dict							       =   jnp.clip(A    ,						a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
									lowercase_ : Union[str, Any]							       =   jnp.log(jnp.clip(A    ,						a_min=1e-20   )   )
							elif variance_type == "fixed_large":
									lowercase_ : Any							       =   state.common.betas[t]
							elif variance_type == "fixed_large_log":
									# Glide max_log
									lowercase_ : Tuple							       =   jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
									return predicted_variance
							elif variance_type == "learned_range":
									lowercase_ : Union[str, Any]							       =   variance
									lowercase_ : Optional[Any]							       =   state.common.betas[t]
									lowercase_ : List[Any]							       =   (predicted_variance + 1) / 2
									lowercase_ : Optional[int]							       =   frac * max_log + (1 - frac) * min_log

							return variance




					def   A  (				self     :						List[Any]    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						int    ,						A     :						jnp.ndarray    ,						A     :						Optional[jax.random.KeyArray] = None    ,						A     :						bool = True    ,						)  ->  Union[FlaxDDPMSchedulerOutput, Tuple]:
							lowercase_ : Any							       =   timestep

							if key is None:
									lowercase_ : str							       =   jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									lowercase_       ,					lowercase_ : Tuple							       =   jnp.split(A    ,						sample.shape[1]    ,						axis=1   )
							else:
									lowercase_ : Optional[int]							       =   None

							# 1. compute alphas, betas
							lowercase_ : Union[str, Any]							       =   state.common.alphas_cumprod[t]
							lowercase_ : int							       =   jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )
							lowercase_ : Union[str, Any]							       =   1 - alpha_prod_t
							lowercase_ : Tuple							       =   1 - alpha_prod_t_prev

							# 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":
									lowercase_ : List[str]							       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
									lowercase_ : Dict							       =   model_output
							elif self.config.prediction_type == "v_prediction":
									lowercase_ : List[str]							       =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
									raise ValueError(
									    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
									    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									lowercase_ : List[str]							       =   jnp.clip(A    ,						-1    ,						1   )

							# 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
							lowercase_ : Dict							       =   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							lowercase_ : List[str]							       =   state.common.alphas[t] ** 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
							lowercase_ : Tuple							       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
									lowercase_ : str							       =   jax.random.split(A    ,						num=1   )
									lowercase_ : List[Any]							       =   jax.random.normal(A    ,						shape=model_output.shape    ,						dtype=self.dtype   )
									return (self._get_variance(A    ,						A    ,						predicted_variance=A   ) ** 0.5) * noise

							lowercase_ : int							       =   jnp.where(t > 0    ,						random_variance()    ,						jnp.zeros(model_output.shape    ,						dtype=self.dtype   )   )

							lowercase_ : Union[str, Any]							       =   pred_prev_sample + variance

							if not return_dict:
									return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=A    ,						state=A   )




					def   A  (				self     :						int    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						)  ->  jnp.ndarray:
							return add_noise_common(state.common    ,						A    ,						A    ,						A   )




					def   A  (				self     :						Dict    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						)  ->  jnp.ndarray:
							return get_velocity_common(state.common    ,						A    ,						A    ,						A   )






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










import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__A									=				logging.get_logger(__name__)

__A									=				"▁"

__A									=				{"vocab_file": "prophetnet.tokenizer"}

__A									=				{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__A									=				{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =collections.OrderedDict()
  with open(__a	,    "r"	,    encoding="utf-8"		) as reader:
    lowerCamelCase__:      int			  =reader.readlines()
  for index, token in enumerate(__a		):
    lowerCamelCase__:      List[str]			  =token.rstrip("\n"		)
    lowerCamelCase__:      List[Any]			  =index
  return vocab






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   List[Any]="[SEP]"       , UpperCAmelCase_				:   Optional[Any]="[SEP]"       , UpperCAmelCase_				:   int="[UNK]"       , UpperCAmelCase_				:   Optional[Any]="[PAD]"       , UpperCAmelCase_				:   Dict="[CLS]"       , UpperCAmelCase_				:   Dict="[MASK]"       , UpperCAmelCase_				:   Optional[Dict[str, Any]] = None       , **UpperCAmelCase_				:   Tuple       , )       ->None:



   '''simple docstring'''



   lowerCamelCase__:      int			  ={} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=UpperCAmelCase_       , eos_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , sp_model_kwargs=self.sp_model_kwargs       , **UpperCAmelCase_       , )

   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

   lowerCamelCase__:      Optional[int]			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))
   lowerCamelCase__:      Optional[int]			  =vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # put special tokens and [unused] tokens into the vocab
   lowerCamelCase__:      Optional[int]			  ={"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

   for i in range(10):
     lowerCamelCase__:      Optional[int]			  =F"""[unused{i}]"""
     lowerCamelCase__:      int			  =5 + i

   # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
   lowerCamelCase__:      int			  =12
   lowerCamelCase__:      Optional[Any]			  ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
   for k in self.fairseq_tokens_to_ids.keys():
     self.unique_no_split_tokens.append(UpperCAmelCase_)

 def __getstate__(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.__dict__.copy()
   lowerCamelCase__:      Dict			  =None
   return state

 def __setstate__(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =d
   try:
     import sentencepiece as spm
   except ImportError:
     logger.warning(
         "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
         " pip install sentencepiece")
     raise

   # for backward compatibility
   if not hasattr(self       , "sp_model_kwargs"):
     lowerCamelCase__:      Dict			  ={}

   lowerCamelCase__:      Tuple			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)

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



   '''simple docstring'''



   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCAmelCase_       , token_ids_a=UpperCAmelCase_       , already_has_special_tokens=UpperCAmelCase_)

   if token_ids_a is None:
     return ([0] * len(UpperCAmelCase_)) + [1]
   return ([0] * len(UpperCAmelCase_)) + [1] + ([0] * len(UpperCAmelCase_)) + [1]

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =[self.sep_token_id]

   if token_ids_a is None:
     return len(token_ids_a + sep) * [0]
   return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Dict:



   '''simple docstring'''



   return len(self.sp_model) + self.fairseq_offset

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      str			  ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   str)       ->str:



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_       , out_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   List[Any])       ->str:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:      str			  =self.sp_model.PieceToId(UpperCAmelCase_)

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

 def 							SCREAMING_SNAKE_CASE_					(self				:   str       , UpperCAmelCase_				:   Optional[Any])       ->Optional[int]:



   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  ="".join(UpperCAmelCase_).replace(UpperCAmelCase_       , " ").strip()
   return out_string

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      List[str]			  =os.path.join(
       UpperCAmelCase_       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file       , UpperCAmelCase_)
   elif not os.path.isfile(self.vocab_file):
     with open(UpperCAmelCase_       , "wb") as fi:
       lowerCamelCase__:      Dict			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return token_ids_a + [self.sep_token_id]
   lowerCamelCase__:      Union[str, Any]			  =[self.sep_token_id]
   return token_ids_a + sep + token_ids_a + sep

