
'''simple docstring'''



import math
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 KarrasDiffusionSchedulers, SchedulerMixin


snake_case_   :  Optional[int]								=      logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    42
			_lowerCAmelCase						=    None




def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : List[str]=0.999 ,   _UpperCamelCase  : Union[str, Any]="cosine" ,   )->   Tuple:






					"""simple docstring"""

					if alpha_transform_type == "cosine":

										def alpha_bar_fn(_UpperCamelCase  : List[str]				):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(_UpperCamelCase  : int				):
															return math.exp(t * -12.0				)

					else:
										raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

					_UpperCamelCase											=   []
					for i in range(_UpperCamelCase				):
										_UpperCamelCase											=   i / num_diffusion_timesteps
										_UpperCamelCase											=   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(_UpperCamelCase				) / alpha_bar_fn(_UpperCamelCase				) ,   _UpperCamelCase				)				)
					return torch.tensor(_UpperCamelCase ,   dtype=torch.floataa				)

class     A_ (  lowerCAmelCase_    ,			lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    [e.name for e in KarrasDiffusionSchedulers]
			_lowerCAmelCase						=    1




			@register_to_config
			def __init__(					self			,     A_ = 10_00			,     A_ = 0.0001			,     A_ = 0.02			,     A_ = "linear"			,     A_ = None			,     A_ = "epsilon"			,     A_ = "linear"			,     A_ = False			,     A_ = "linspace"			,     A_ = 0			,     ):
								if trained_betas is not None:
													_UpperCamelCase											=   torch.tensor(A_			,     dtype=torch.floataa   )
								elif beta_schedule == "linear":
													_UpperCamelCase											=   torch.linspace(A_			,     A_			,     A_			,     dtype=torch.floataa   )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase											=   (
													    torch.linspace(beta_start**0.5			,     beta_end**0.5			,     A_			,     dtype=torch.floataa   ) ** 2
													)
								elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase											=   betas_for_alpha_bar(A_   )
								else:
													raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

								_UpperCamelCase											=   1.0 - self.betas
								_UpperCamelCase											=   torch.cumprod(self.alphas			,     dim=0   )

								_UpperCamelCase											=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
								_UpperCamelCase											=   np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
								_UpperCamelCase											=   torch.from_numpy(A_   )

								# setable values
								_UpperCamelCase											=   None
								_UpperCamelCase											=   np.linspace(0			,     num_train_timesteps - 1			,     A_			,     dtype=A_   )[::-1].copy()
								_UpperCamelCase											=   torch.from_numpy(A_   )
								_UpperCamelCase											=   False
								_UpperCamelCase											=   use_karras_sigmas




			@property
			def       a	(					self   ):
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5




			def       a	(					self			,     A_			,     A_   ):
								if isinstance(A_			,     torch.Tensor   ):
													_UpperCamelCase											=   timestep.to(self.timesteps.device   )
								_UpperCamelCase											=   (self.timesteps == timestep).nonzero().item()
								_UpperCamelCase											=   self.sigmas[step_index]

								_UpperCamelCase											=   sample / ((sigma**2 + 1) ** 0.5)

								_UpperCamelCase											=   True
								return sample




			def       a	(					self			,     A_			,     A_ = None   ):
								_UpperCamelCase											=   num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
													_UpperCamelCase											=   np.linspace(0			,     self.config.num_train_timesteps - 1			,     A_			,     dtype=A_   )[
													    ::-1
													].copy()
								elif self.config.timestep_spacing == "leading":
													_UpperCamelCase											=   self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase											=   (np.arange(0			,     A_   ) * step_ratio).round()[::-1].copy().astype(A_   )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase											=   self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase											=   (np.arange(self.config.num_train_timesteps			,     0			,     -step_ratio   )).round().copy().astype(A_   )
													timesteps -= 1
								else:
													raise ValueError(
													    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

								_UpperCamelCase											=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
								_UpperCamelCase											=   np.log(A_   )

								if self.config.interpolation_type == "linear":
													_UpperCamelCase											=   np.interp(A_			,     np.arange(0			,     len(A_   )   )			,     A_   )
								elif self.config.interpolation_type == "log_linear":
													_UpperCamelCase											=   torch.linspace(np.log(sigmas[-1]   )			,     np.log(sigmas[0]   )			,     num_inference_steps + 1   ).exp()
								else:
													raise ValueError(
													    F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
													    " 'linear' or 'log_linear'"   )

								if self.use_karras_sigmas:
													_UpperCamelCase											=   self._convert_to_karras(in_sigmas=A_			,     num_inference_steps=self.num_inference_steps   )
													_UpperCamelCase											=   np.array([self._sigma_to_t(A_			,     A_   ) for sigma in sigmas]   )

								_UpperCamelCase											=   np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
								_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_   )
								if str(A_   ).startswith("mps"   ):
													# mps does not support float64
													_UpperCamelCase											=   torch.from_numpy(A_   ).to(A_			,     dtype=torch.floataa   )
								else:
													_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_   )




			def       a	(					self			,     A_			,     A_   ):
								# get log sigma
								_UpperCamelCase											=   np.log(A_   )

								# get distribution
								_UpperCamelCase											=   log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								_UpperCamelCase											=   np.cumsum((dists >= 0)			,     axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
								_UpperCamelCase											=   low_idx + 1

								_UpperCamelCase											=   log_sigmas[low_idx]
								_UpperCamelCase											=   log_sigmas[high_idx]

								# interpolate sigmas
								_UpperCamelCase											=   (low - log_sigma) / (low - high)
								_UpperCamelCase											=   np.clip(A_			,     0			,     1   )

								# transform interpolation to time range
								_UpperCamelCase											=   (1 - w) * low_idx + w * high_idx
								_UpperCamelCase											=   t.reshape(sigma.shape   )
								return t




			def       a	(					self			,     A_			,     A_   ):
								_UpperCamelCase											=   in_sigmas[-1].item()
								_UpperCamelCase											=   in_sigmas[0].item()

								_UpperCamelCase											=   7.0  # 7.0 is the value used in the paper
								_UpperCamelCase											=   np.linspace(0			,     1			,     A_   )
								_UpperCamelCase											=   sigma_min ** (1 / rho)
								_UpperCamelCase											=   sigma_max ** (1 / rho)
								_UpperCamelCase											=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




			def       a	(					self			,     A_			,     A_			,     A_			,     A_ = 0.0			,     A_ = 0.0			,     A_ = float("inf"   )			,     A_ = 1.0			,     A_ = None			,     A_ = True			,     ):

								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"
													        " `EulerDiscreteScheduler.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   ):
													_UpperCamelCase											=   timestep.to(self.timesteps.device   )

								_UpperCamelCase											=   (self.timesteps == timestep).nonzero().item()
								_UpperCamelCase											=   self.sigmas[step_index]

								_UpperCamelCase											=   min(s_churn / (len(self.sigmas   ) - 1)			,     2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

								_UpperCamelCase											=   randn_tensor(
								    model_output.shape			,     dtype=model_output.dtype			,     device=model_output.device			,     generator=A_   )

								_UpperCamelCase											=   noise * s_noise
								_UpperCamelCase											=   sigma * (gamma + 1)

								if gamma > 0:
													_UpperCamelCase											=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													_UpperCamelCase											=   model_output
								elif self.config.prediction_type == "epsilon":
													_UpperCamelCase											=   sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													_UpperCamelCase											=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
													raise ValueError(
													    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

								# 2. Convert to an ODE derivative
								_UpperCamelCase											=   (sample - pred_original_sample) / sigma_hat

								_UpperCamelCase											=   self.sigmas[step_index + 1] - sigma_hat

								_UpperCamelCase											=   sample + derivative * dt

								if not return_dict:
													return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=A_			,     pred_original_sample=A_   )




			def       a	(					self			,     A_			,     A_			,     A_			,     ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								_UpperCamelCase											=   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
													_UpperCamelCase											=   self.timesteps.to(original_samples.device			,     dtype=torch.floataa   )
													_UpperCamelCase											=   timesteps.to(original_samples.device			,     dtype=torch.floataa   )
								else:
													_UpperCamelCase											=   self.timesteps.to(original_samples.device   )
													_UpperCamelCase											=   timesteps.to(original_samples.device   )

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

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

								_UpperCamelCase											=   original_samples + noise * sigma
								return noisy_samples




			def __len__(					self   ):
								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			,     A_			,     A_			,     A_ = None			,     A_ = None			,     A_ = None			,     A_ = "longest"			,     A_ = True			,     A_ = 5_12			,     A_ = None			,     A_ = True			,     A_ = True			,     A_ = True			,     **A_			,     ):
								super().__init__()
								if use_fast_bert_tokenizer:
													_UpperCamelCase											=   FastBertTokenizer(
													    A_			,     token_out_type=tf.intaa			,     lower_case_nfd_strip_accents=A_			,     **A_   )
								else:
													_UpperCamelCase											=   tf.lookup.StaticVocabularyTable(
													    tf.lookup.KeyValueTensorInitializer(
													        keys=A_			,     key_dtype=tf.string			,     values=tf.range(tf.size(A_			,     out_type=tf.intaa   )			,     dtype=tf.intaa   )			,     value_dtype=tf.intaa			,     )			,     num_oov_buckets=1			,     )
													_UpperCamelCase											=   BertTokenizerLayer(
													    A_			,     token_out_type=tf.intaa			,     lower_case=A_			,     **A_   )

								_UpperCamelCase											=   vocab_list
								_UpperCamelCase											=   do_lower_case
								_UpperCamelCase											=   cls_token_id or vocab_list.index("[CLS]"   )
								_UpperCamelCase											=   sep_token_id or vocab_list.index("[SEP]"   )
								_UpperCamelCase											=   pad_token_id or vocab_list.index("[PAD]"   )
								_UpperCamelCase											=   ShrinkLongestTrimmer(max_length - 3			,     axis=1   )  # Allow room for special tokens
								_UpperCamelCase											=   max_length
								_UpperCamelCase											=   padding
								_UpperCamelCase											=   truncation
								_UpperCamelCase											=   pad_to_multiple_of
								_UpperCamelCase											=   return_token_type_ids
								_UpperCamelCase											=   return_attention_mask




			@classmethod
			def       a	(					cls			,     A_			,     **A_   ):  # noqa: F821
								_UpperCamelCase											=   kwargs.pop("do_lower_case"			,     A_   )
								_UpperCamelCase											=   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								_UpperCamelCase											=   kwargs.pop("cls_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								_UpperCamelCase											=   kwargs.pop("sep_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								_UpperCamelCase											=   kwargs.pop("pad_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								_UpperCamelCase											=   tokenizer.get_vocab()
								_UpperCamelCase											=   sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,     key=lambda A_   : x[1]   )
								_UpperCamelCase											=   [entry[0] for entry in vocab]
								return cls(
								    vocab_list=A_			,     do_lower_case=A_			,     cls_token_id=A_			,     sep_token_id=A_			,     pad_token_id=A_			,     **A_			,     )




			@classmethod
			def       a	(					cls			,     A_			,     *A_			,     **A_   ):
								try:
													_UpperCamelCase											=   BertTokenizer.from_pretrained(A_			,     *A_			,     **A_   )
								except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													_UpperCamelCase											=   BertTokenizerFast.from_pretrained(A_			,     *A_			,     **A_   )
								return cls.from_tokenizer(A_			,     **A_   )




			def       a	(					self			,     A_   ):
								if self.do_lower_case:
													_UpperCamelCase											=   case_fold_utfa(A_   )
								_UpperCamelCase											=   self.tf_tokenizer.tokenize(A_   )
								return tokens.merge_dims(1			,     -1   )




			def       a	(					self			,     A_			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     ):
								if padding is None:
													_UpperCamelCase											=   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:
													_UpperCamelCase											=   self.max_length
								if truncation is None:
													_UpperCamelCase											=   self.truncation
								if pad_to_multiple_of is None:
													_UpperCamelCase											=   self.pad_to_multiple_of
								if return_token_type_ids is None:
													_UpperCamelCase											=   self.return_token_type_ids
								if return_attention_mask is None:
													_UpperCamelCase											=   self.return_attention_mask
								if not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								if text_pair is not None and not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								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:
													_UpperCamelCase				,	_UpperCamelCase											=   text[:, 0], text[:, 1]
								_UpperCamelCase											=   self.unpaired_tokenize(A_   )
								if text_pair is None:  # Unpaired text
													if truncation:
																		_UpperCamelCase											=   text[:, : max_length - 2]  # Allow room for special tokens
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text,)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								else:  # Paired text
													_UpperCamelCase											=   self.unpaired_tokenize(A_   )
													if truncation:
																		_UpperCamelCase				,	_UpperCamelCase											=   self.paired_trimmer.trim([text, text_pair]   )
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text, text_pair)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								if padding == "longest":
													_UpperCamelCase											=   input_ids.bounding_shape(axis=1   )
													if pad_to_multiple_of is not None:
																		# No ceiling division in tensorflow, so we negate floordiv instead
																		_UpperCamelCase											=   pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,     A_   ))
								else:
													_UpperCamelCase											=   max_length

								_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
								_UpperCamelCase											=   {"input_ids": input_ids}
								if return_attention_mask:
													_UpperCamelCase											=   attention_mask
								if return_token_type_ids:
													_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(
													    A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
													_UpperCamelCase											=   token_type_ids
								return output




			def       a	(					self   ):
								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,
								}



