

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      List[Any]=0.999       ,     SCREAMING_SNAKE_CASE__     :      Tuple="cosine"       ,     )				-> Dict:





    '''simple docstring'''






    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__     :      Optional[Any] ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

    elif alpha_transform_type == "exp":

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

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

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


class 					A		(				_UpperCAmelCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				[e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase						      =				2


    @register_to_config
    def __init__( self					:			List[str],lowercase_					:			int = 1_0_0_0,lowercase_					:			float = 0.00_085,lowercase_					:			float = 0.012,lowercase_					:			str = "linear",lowercase_					:			Optional[Union[np.ndarray, List[float]]] = None,lowercase_					:			str = "epsilon",lowercase_					:			str = "linspace",lowercase_					:			int = 0,)->       Any:





        '''simple docstring'''







        if trained_betas is not None:
            A__        =  torch.tensor(lowercase_,dtype=torch.floataa						)
        elif beta_schedule == "linear":
            A__        =  torch.linspace(lowercase_,lowercase_,lowercase_,dtype=torch.floataa						)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            A__        =  (
                torch.linspace(beta_start**0.5,beta_end**0.5,lowercase_,dtype=torch.floataa						) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            A__        =  betas_for_alpha_bar(lowercase_						)
        else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'						)

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

        #  set all values
        self.set_timesteps(lowercase_,lowercase_,lowercase_						)


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			int,lowercase_					:			List[Any]=None						)->       List[str]:





        '''simple docstring'''







        if schedule_timesteps is None:
            A__        =  self.timesteps

        A__        =  (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter						) == 0:
            A__        =  1 if len(lowercase_						) > 1 else 0
        else:
            A__        =  timestep.cpu().item() if torch.is_tensor(lowercase_						) else timestep
            A__        =  self._index_counter[timestep_int]

        return indices[pos].item()


    @property
    def 		snake_case__	( self					:			Optional[Any]						)->       List[Any]:





        '''simple docstring'''







        if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

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


    def 		snake_case__	( self					:			int,lowercase_					:			torch.FloatTensor,lowercase_					:			Union[float, torch.FloatTensor],)->       torch.FloatTensor:





        '''simple docstring'''







        A__        =  self.index_for_timestep(lowercase_						)

        if self.state_in_first_order:
            A__        =  self.sigmas[step_index]
        else:
            A__        =  self.sigmas_interpol[step_index - 1]

        A__        =  sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 		snake_case__	( self					:			List[Any],lowercase_					:			int,lowercase_					:			Union[str, torch.device] = None,lowercase_					:			Optional[int] = None,)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  num_inference_steps

        A__        =  num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            A__        =  np.linspace(0,num_train_timesteps - 1,lowercase_,dtype=lowercase_						)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            A__        =  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
            A__        =  (np.arange(0,lowercase_						) * step_ratio).round()[::-1].copy().astype(lowercase_						)
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            A__        =  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
            A__        =  (np.arange(lowercase_,0,-step_ratio						)).round().copy().astype(lowercase_						)
            timesteps -= 1
        else:
            raise ValueError(
                F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'						)

        A__        =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
        A__        =  torch.from_numpy(np.log(lowercase_						)						).to(lowercase_						)

        A__        =  np.interp(lowercase_,np.arange(0,len(lowercase_						)						),lowercase_						)
        A__        =  np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
        A__        =  torch.from_numpy(lowercase_						).to(device=lowercase_						)

        # compute up and down sigmas
        A__        =  sigmas.roll(-1						)
        A__        =  0.0
        A__        =  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        A__        =  (sigmas_next**2 - sigmas_up**2) ** 0.5
        A__        =  0.0

        # compute interpolated sigmas
        A__        =  sigmas.log().lerp(sigmas_down.log(),0.5						).exp()
        A__        =  0.0

        # set sigmas
        A__        =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
        A__        =  torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
        A__        =  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
        A__        =  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

        if str(lowercase_						).startswith('mps'						):
            # mps does not support float64
            A__        =  torch.from_numpy(lowercase_						).to(lowercase_,dtype=torch.floataa						)
        else:
            A__        =  torch.from_numpy(lowercase_						).to(lowercase_						)

        A__        =  self.sigma_to_t(lowercase_						).to(lowercase_,dtype=timesteps.dtype						)
        A__        =  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]),dim=-1						).flatten()

        A__        =  torch.cat([timesteps[:1], interleaved_timesteps]						)

        A__        =  None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        A__        =  defaultdict(lowercase_						)


    def 		snake_case__	( self					:			str,lowercase_					:			int						)->       Optional[int]:





        '''simple docstring'''







        A__        =  sigma.log()

        # get distribution
        A__        =  log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        A__        =  dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
        A__        =  low_idx + 1

        A__        =  self.log_sigmas[low_idx]
        A__        =  self.log_sigmas[high_idx]

        # interpolate sigmas
        A__        =  (low - log_sigma) / (low - high)
        A__        =  w.clamp(0,1						)

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


    @property
    def 		snake_case__	( self					:			Optional[Any]						)->       Optional[Any]:





        '''simple docstring'''







        return self.sample is None


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			Union[torch.FloatTensor, np.ndarray],lowercase_					:			Union[float, torch.FloatTensor],lowercase_					:			Union[torch.FloatTensor, np.ndarray],lowercase_					:			Optional[torch.Generator] = None,lowercase_					:			bool = True,)->       Union[SchedulerOutput, Tuple]:





        '''simple docstring'''







        A__        =  self.index_for_timestep(lowercase_						)

        # advance index counter by 1
        A__        =  timestep.cpu().item() if torch.is_tensor(lowercase_						) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            A__        =  self.sigmas[step_index]
            A__        =  self.sigmas_interpol[step_index]
            A__        =  self.sigmas_up[step_index]
            A__        =  self.sigmas_down[step_index - 1]
        else:
            # 2nd order / KPDM2's method
            A__        =  self.sigmas[step_index - 1]
            A__        =  self.sigmas_interpol[step_index - 1]
            A__        =  self.sigmas_up[step_index - 1]
            A__        =  self.sigmas_down[step_index - 1]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        A__        =  0
        A__        =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        A__        =  model_output.device
        A__        =  randn_tensor(model_output.shape,dtype=model_output.dtype,device=lowercase_,generator=lowercase_						)

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            A__        =  sigma_hat if self.state_in_first_order else sigma_interpol
            A__        =  sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            A__        =  sigma_hat if self.state_in_first_order else sigma_interpol
            A__        =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            raise NotImplementedError('prediction_type not implemented yet: sample'						)
        else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            A__        =  (sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            A__        =  sigma_interpol - sigma_hat

            # store for 2nd order step
            A__        =  sample
            A__        =  dt
            A__        =  sample + derivative * dt
        else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            A__        =  (sample - pred_original_sample) / sigma_interpol
            # 3. delta timestep
            A__        =  sigma_down - sigma_hat

            A__        =  self.sample
            A__        =  None

            A__        =  sample + derivative * dt
            A__        =  prev_sample + noise * sigma_up

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=lowercase_						)


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			torch.FloatTensor,lowercase_					:			torch.FloatTensor,lowercase_					:			torch.FloatTensor,)->       torch.FloatTensor:





        '''simple docstring'''







        A__        =  self.sigmas.to(device=original_samples.device,dtype=original_samples.dtype						)
        if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_						):
            # mps does not support float64
            A__        =  self.timesteps.to(original_samples.device,dtype=torch.floataa						)
            A__        =  timesteps.to(original_samples.device,dtype=torch.floataa						)
        else:
            A__        =  self.timesteps.to(original_samples.device						)
            A__        =  timesteps.to(original_samples.device						)

        A__        =  [self.index_for_timestep(lowercase_,lowercase_						) for t in timesteps]

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

        A__        =  original_samples + noise * sigma
        return noisy_samples


    def __len__( self					:			str						)->       List[str]:





        '''simple docstring'''







        return self.config.num_train_timesteps







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       _UpperCamelCase   (					tf.keras.layers.Layer		):
      def __init__(      self :int			,   lowerCamelCase :List			,   lowerCamelCase :bool			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :str = "longest"			,   lowerCamelCase :bool = True			,   lowerCamelCase :int = 512			,   lowerCamelCase :int = None			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   **lowerCamelCase :Optional[Any]			,   )  ->      List[str]:
       super().__init__()
       if use_fast_bert_tokenizer:
        UpperCAmelCase__							=    FastBertTokenizer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case_nfd_strip_accents=lowerCamelCase			,   **lowerCamelCase   )
       else:
        UpperCAmelCase__							=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=lowerCamelCase			,   key_dtype=tf.string			,   values=tf.range(tf.size(lowerCamelCase			,   out_type=tf.intaa   )			,   dtype=tf.intaa   )			,   value_dtype=tf.intaa			,   )			,   num_oov_buckets=1			,   )
        UpperCAmelCase__							=    BertTokenizerLayer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case=lowerCamelCase			,   **lowerCamelCase   )

       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 						UpperCAmelCase_    (      cls :List[str]			,   lowerCamelCase :"PreTrainedTokenizerBase"			,   **lowerCamelCase :Dict   )  ->      Dict:  # noqa: F821
       UpperCAmelCase__							=    kwargs.pop("do_lower_case"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       UpperCAmelCase__							=    kwargs.pop("cls_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       UpperCAmelCase__							=    kwargs.pop("sep_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       UpperCAmelCase__							=    kwargs.pop("pad_token_id"			,   lowerCamelCase   )
       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 lowerCamelCase   : x[1]   )
       UpperCAmelCase__							=    [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowerCamelCase			,   do_lower_case=lowerCamelCase			,   cls_token_id=lowerCamelCase			,   sep_token_id=lowerCamelCase			,   pad_token_id=lowerCamelCase			,   **lowerCamelCase			,   )
      @classmethod
      def 						UpperCAmelCase_    (      cls :Any			,   lowerCamelCase :Union[str, os.PathLike]			,   *lowerCamelCase :str			,   **lowerCamelCase :List[str]   )  ->      Tuple:
       try:
        UpperCAmelCase__							=    BertTokenizer.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        UpperCAmelCase__							=    BertTokenizerFast.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       return cls.from_tokenizer(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Optional[int]   )  ->      List[str]:
       if self.do_lower_case:
        UpperCAmelCase__							=    case_fold_utfa(lowerCamelCase   )
       UpperCAmelCase__							=    self.tf_tokenizer.tokenize(lowerCamelCase   )
       return tokens.merge_dims(1			,   -1   )
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Tuple			,   lowerCamelCase :str=None			,   lowerCamelCase :Tuple=None			,   lowerCamelCase :Any=None			,   lowerCamelCase :Optional[int]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Tuple=None			,   )  ->      str:
       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(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       if text_pair is not None and not isinstance(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       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(lowerCamelCase   )
       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(lowerCamelCase   )
        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			,   lowerCamelCase   ))
       else:
        UpperCAmelCase__							=    max_length

       UpperCAmelCase__			,			UpperCAmelCase__							=    pad_model_inputs(lowerCamelCase			,   max_seq_length=lowerCamelCase			,   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(
            lowerCamelCase			,   max_seq_length=lowerCamelCase			,   pad_value=self.pad_token_id   )
        UpperCAmelCase__							=    token_type_ids
       return output







      def 						UpperCAmelCase_    (      self :Tuple   )  ->      Tuple:
       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,
       }





