



"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     CommonSchedulerState
					_A						:     jnp.ndarray

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






					@classmethod
					def   lowerCAmelCase_							(			cls:				str		,	snake_case:				CommonSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      str:
											return cls(
											    common=snake_case		,	final_alpha_cumprod=snake_case		,	init_noise_sigma=snake_case		,	timesteps=snake_case		,	)

@dataclass
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     DDIMSchedulerState

class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	[e.name for e in FlaxKarrasDiffusionSchedulers]

					_A						:     jnp.dtype






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      Optional[Any]:
											return True






					@register_to_config
					def __init__(			self:				Dict		,	snake_case:				int = 1_000		,	snake_case:				float = 0.0_0_0_1		,	snake_case:				float = 0.0_2		,	snake_case:				str = "linear"		,	snake_case:				Optional[jnp.ndarray] = None		,	snake_case:				bool = True		,	snake_case:				int = 0		,	snake_case:				str = "epsilon"		,	snake_case:				jnp.dtype = jnp.floataa		,	)  ->      int:
											snake_case_					:int		=     dtype






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				Optional[CommonSchedulerState] = None		)  ->      DDIMSchedulerState:
											if common is None:
																	snake_case_					:str		=     CommonSchedulerState.create(self		)

											# At every step in ddim, we are looking into the previous alphas_cumprod
											# For the final step, there is no previous alphas_cumprod because we are already at 0
											# `set_alpha_to_one` decides whether we set this parameter simply to one or
											# whether we use the final alpha of the "non-previous" one.
											snake_case_					:Union[str, Any]		=     (
											    jnp.array(1.0		,	dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
											)

											# standard deviation of the initial noise distribution
											snake_case_					:Optional[Any]		=     jnp.array(1.0		,	dtype=self.dtype		)

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

											return DDIMSchedulerState.create(
											    common=snake_case		,	final_alpha_cumprod=snake_case		,	init_noise_sigma=snake_case		,	timesteps=snake_case		,	)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				Optional[int] = None		)  ->      jnp.ndarray:
											return sample






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				int		,	snake_case:				Tuple = ()		)  ->      DDIMSchedulerState:
											snake_case_					:str		=     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
											snake_case_					:List[str]		=     (jnp.arange(0		,	snake_case		) * step_ratio).round()[::-1] + self.config.steps_offset

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






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				Union[str, Any]		,	snake_case:				int		)  ->      str:
											snake_case_					:List[Any]		=     state.common.alphas_cumprod[timestep]
											snake_case_					:Union[str, Any]		=     jnp.where(
											    prev_timestep >= 0		,	state.common.alphas_cumprod[prev_timestep]		,	state.final_alpha_cumprod		)
											snake_case_					:Dict		=     1 - alpha_prod_t
											snake_case_					:List[str]		=     1 - alpha_prod_t_prev

											snake_case_					:List[Any]		=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				int		,	snake_case:				jnp.ndarray		,	snake_case:				float = 0.0		,	snake_case:				bool = True		,	)  ->      Union[FlaxDDIMSchedulerOutput, Tuple]:
											if state.num_inference_steps is None:
																	raise ValueError(
																	    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""		)

											# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
											# Ideally, read DDIM paper in-detail understanding

											# Notation (<variable name> -> <name in paper>
											# - pred_noise_t -> e_theta(x_t, t)
											# - pred_original_sample -> f_theta(x_t, t) or x_0
											# - std_dev_t -> sigma_t
											# - eta -> η
											# - pred_sample_direction -> "direction pointing to x_t"
											# - pred_prev_sample -> "x_t-1"

											# 1. get previous step value (=t-1)
											snake_case_					:Any		=     timestep - self.config.num_train_timesteps // state.num_inference_steps

											snake_case_					:Union[str, Any]		=     state.common.alphas_cumprod
											snake_case_					:int		=     state.final_alpha_cumprod

											# 2. compute alphas, betas
											snake_case_					:int		=     alphas_cumprod[timestep]
											snake_case_					:Optional[int]		=     jnp.where(prev_timestep >= 0		,	alphas_cumprod[prev_timestep]		,	snake_case		)

											snake_case_					:List[Any]		=     1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																	snake_case_					:str		=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	snake_case_					:Any		=     model_output
											elif self.config.prediction_type == "sample":
																	snake_case_					:Optional[Any]		=     model_output
																	snake_case_					:List[Any]		=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																	snake_case_					:str		=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	snake_case_					:Union[str, Any]		=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																	    """ `v_prediction`"""		)

											# 4. compute variance: "sigma_t(η)" -> see formula (16)
											# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
											snake_case_					:Optional[int]		=     self._get_variance(snake_case		,	snake_case		,	snake_case		)
											snake_case_					:Union[str, Any]		=     eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_					:Any		=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_					:Optional[int]		=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																	return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=snake_case		,	state=snake_case		)






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return add_noise_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return get_velocity_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






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




"""simple docstring"""






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class 	A_       (     tf.keras.layers.Layer   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "longest"  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = 5_12  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					**__UpperCAmelCase  ,					)				->					Union[str, Any]:
     super().__init__()
     if use_fast_bert_tokenizer:
        a   :	int						=    FastBertTokenizer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case_nfd_strip_accents=__UpperCAmelCase  ,					**__UpperCAmelCase						)
     else:
        a   :	Optional[Any]						=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=__UpperCAmelCase  ,					key_dtype=tf.string  ,					values=tf.range(tf.size(__UpperCAmelCase  ,					out_type=tf.intaa						)  ,					dtype=tf.intaa						)  ,					value_dtype=tf.intaa  ,					)  ,					num_oov_buckets=1  ,					)
        a   :	str						=    BertTokenizerLayer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case=__UpperCAmelCase  ,					**__UpperCAmelCase						)

     a   :	str						=    vocab_list
     a   :	Dict						=    do_lower_case
     a   :	Dict						=    cls_token_id or vocab_list.index('[CLS]'						)
     a   :	Tuple						=    sep_token_id or vocab_list.index('[SEP]'						)
     a   :	Optional[Any]						=    pad_token_id or vocab_list.index('[PAD]'						)
     a   :	Tuple						=    ShrinkLongestTrimmer(max_length - 3  ,					axis=1						)  # Allow room for special tokens
     a   :	Union[str, Any]						=    max_length
     a   :	Tuple						=    padding
     a   :	List[Any]						=    truncation
     a   :	Union[str, Any]						=    pad_to_multiple_of
     a   :	List[Any]						=    return_token_type_ids
     a   :	Optional[int]						=    return_attention_mask
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:  # noqa: F821
     a   :	List[Any]						=    kwargs.pop('do_lower_case'  ,					__UpperCAmelCase						)
     a   :	str						=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     a   :	List[Any]						=    kwargs.pop('cls_token_id'  ,					__UpperCAmelCase						)
     a   :	int						=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     a   :	Tuple						=    kwargs.pop('sep_token_id'  ,					__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     a   :	List[Any]						=    kwargs.pop('pad_token_id'  ,					__UpperCAmelCase						)
     a   :	Any						=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     a   :	Optional[Any]						=    tokenizer.get_vocab()
     a   :	Optional[Any]						=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  ,					key=lambda __UpperCAmelCase						: x[1]						)
     a   :	Optional[Any]						=    [entry[0] for entry in vocab]
     return cls(
         vocab_list=__UpperCAmelCase  ,					do_lower_case=__UpperCAmelCase  ,					cls_token_id=__UpperCAmelCase  ,					sep_token_id=__UpperCAmelCase  ,					pad_token_id=__UpperCAmelCase  ,					**__UpperCAmelCase  ,					)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:
     try:
        a   :	Dict						=    BertTokenizer.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        a   :	Tuple						=    BertTokenizerFast.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     return cls.from_tokenizer(__UpperCAmelCase  ,					**__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     if self.do_lower_case:
        a   :	int						=    case_fold_utfa(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    self.tf_tokenizer.tokenize(__UpperCAmelCase						)
     return tokens.merge_dims(1  ,					-1						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					)				->					Optional[int]:
     if padding is None:
        a   :	List[Any]						=    self.padding
     if padding not in ("longest", "max_length"):
        raise ValueError('Padding must be either \'longest\' or \'max_length\'!'						)
     if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'						)
     if max_length is None:
        a   :	Any						=    self.max_length
     if truncation is None:
        a   :	Any						=    self.truncation
     if pad_to_multiple_of is None:
        a   :	Optional[int]						=    self.pad_to_multiple_of
     if return_token_type_ids is None:
        a   :	str						=    self.return_token_type_ids
     if return_attention_mask is None:
        a   :	Dict						=    self.return_attention_mask
     if not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	List[str]						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None and not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	Any						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None:
        if text.shape.rank > 1:
           raise ValueError('text argument should not be multidimensional when a text pair is supplied!'						)
        if text_pair.shape.rank > 1:
           raise ValueError('text_pair should not be multidimensional!'						)
     if text.shape.rank == 2:
        a	, a   :	Union[str, Any]						=    text[:, 0], text[:, 1]
     a   :	Union[str, Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
     if text_pair is None:  # Unpaired text
        if truncation:
           a   :	List[Any]						=    text[:, : max_length - 2]  # Allow room for special tokens
        a	, a   :	Dict						=    combine_segments(
            (text,)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     else:  # Paired text
        a   :	Optional[Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
        if truncation:
           a	, a   :	List[str]						=    self.paired_trimmer.trim([text, text_pair]						)
        a	, a   :	List[Any]						=    combine_segments(
            (text, text_pair)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     if padding == "longest":
        a   :	List[Any]						=    input_ids.bounding_shape(axis=1						)
        if pad_to_multiple_of is not None:
           # No ceiling division in tensorflow, so we negate floordiv instead
           a   :	Optional[int]						=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length  ,					__UpperCAmelCase						))
     else:
        a   :	Any						=    max_length

     a	, a   :	Any						=    pad_model_inputs(__UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
     a   :	Optional[Any]						=    {'input_ids': input_ids}
     if return_attention_mask:
        a   :	Tuple						=    attention_mask
     if return_token_type_ids:
        a	, a   :	int						=    pad_model_inputs(
            __UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
        a   :	Union[str, Any]						=    token_type_ids
     return output






  def 							lowercase_     (						self						)				->					List[str]:
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }





