



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  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      CommonSchedulerState

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


							@classmethod
							def _a      (							cls  ,  a_  ,  a_  ,  a_		):
											return cls(common=a_  ,  init_noise_sigma=a_  ,  timesteps=a_		)




@dataclass
class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      DDPMSchedulerState




class  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					[e.name for e in FlaxKarrasDiffusionSchedulers]

							UpperCamelCase__				:      jnp.dtype


							@property
							def _a      (							self		):
											return True


							@register_to_config
							def __init__(							self  ,  a_ = 1_000  ,  a_ = 0.0_001  ,  a_ = 0.02  ,  a_ = "linear"  ,  a_ = None  ,  a_ = "fixed_small"  ,  a_ = True  ,  a_ = "epsilon"  ,  a_ = jnp.floataa  ,  ):
											a__				      =		dtype


							def _a      (							self  ,  a_ = None		):




											if common is None:
															a__				      =		CommonSchedulerState.create(self		)

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

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

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


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None		):
											return sample


							def _a      (							self  ,  a_  ,  a_  ,  a_ = ()		):
											a__				      =		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
											a__				      =		(jnp.arange(0  ,  a_		) * step_ratio).round()[::-1]

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


							def _a      (							self  ,  a_  ,  a_  ,  a_=None  ,  a_=None		):
											a__				      =		state.common.alphas_cumprod[t]
											a__				      =		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
											a__				      =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
															a__				      =		jnp.clip(a_  ,  a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
															a__				      =		jnp.log(jnp.clip(a_  ,  a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
															a__				      =		state.common.betas[t]
											elif variance_type == "fixed_large_log":
															# Glide max_log
															a__				      =		jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
															return predicted_variance
											elif variance_type == "learned_range":
															a__				      =		variance
															a__				      =		state.common.betas[t]
															a__				      =		(predicted_variance + 1) / 2
															a__				      =		frac * max_log + (1 - frac) * min_log

											return variance


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_ = None  ,  a_ = True  ,  ):
											a__				      =		timestep

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

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

											# 1. compute alphas, betas
											a__				      =		state.common.alphas_cumprod[t]
											a__				      =		jnp.where(t > 0  ,  state.common.alphas_cumprod[t - 1]  ,  jnp.array(1.0  ,  dtype=self.dtype		)		)
											a__				      =		1 - alpha_prod_t
											a__				      =		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":
															a__				      =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
															a__				      =		model_output
											elif self.config.prediction_type == "v_prediction":
															a__				      =		(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:
															a__				      =		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
											a__				      =		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											a__				      =		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
											a__				      =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
															a__				      =		jax.random.split(a_  ,  num=1		)
															a__				      =		jax.random.normal(a_  ,  shape=model_output.shape  ,  dtype=self.dtype		)
															return (self._get_variance(a_  ,  a_  ,  predicted_variance=a_		) ** 0.5) * noise

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

											a__				      =		pred_prev_sample + variance

											if not return_dict:
															return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=a_  ,  state=a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  ):
											return add_noise_common(state.common  ,  a_  ,  a_  ,  a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  ):
											return get_velocity_common(state.common  ,  a_  ,  a_  ,  a_		)







							def __len__(							self		):
											return self.config.num_train_timesteps






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

import sentencepiece as spm

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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

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

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

											return (out_vocab_file,)

