'''simple docstring'''




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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__=0.9_9_9				,  UpperCamelCase__="cosine"				,  )	->    Optional[int]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase__				):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase__				):
            return math.exp(t * -1_2.0				)

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

    __lowerCamelCase		=  []
    for i in range(UpperCamelCase__				):
        __lowerCamelCase		=  i / num_diffusion_timesteps
        __lowerCamelCase		=  (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__      (   UpperCAmelCase__							,   UpperCAmelCase__ ):
     lowerCamelCase  :     Dict							=[e.name for e in KarrasDiffusionSchedulers]
     lowerCamelCase  :     Optional[Any]							=2






     @register_to_config
     def __init__( self		:		Optional[Any]  ,				a		:		int = 10_00  ,				a		:		float = 0.0_00_85  ,				a		:		float = 0.0_12  ,				a		:		str = "linear"  ,				a		:		Optional[Union[np.ndarray, List[float]]] = None  ,				a		:		str = "epsilon"  ,				a		:		Optional[bool] = False  ,				a		:		Optional[bool] = False  ,				a		:		float = 1.0  ,				a		:		str = "linspace"  ,				a		:		int = 0  ,				):





         """simple docstring"""



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

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

         #  set all values
         self.set_timesteps(a  ,				a  ,				a    )
         __lowerCamelCase		=  use_karras_sigmas






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict  ,				a		:		Optional[Any]  ,				a		:		List[Any]=None    ):





         """simple docstring"""



         if schedule_timesteps is None:
             __lowerCamelCase		=  self.timesteps

         __lowerCamelCase		=  (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:
             __lowerCamelCase		=  1 if len(a    ) > 1 else 0
         else:
             __lowerCamelCase		=  timestep.cpu().item() if torch.is_tensor(a    ) else timestep
             __lowerCamelCase		=  self._index_counter[timestep_int]

         return indices[pos].item()






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



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

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		torch.FloatTensor  ,				a		:		Union[float, torch.FloatTensor]  ,				):





         """simple docstring"""



         __lowerCamelCase		=  self.index_for_timestep(a    )

         __lowerCamelCase		=  self.sigmas[step_index]
         __lowerCamelCase		=  sample / ((sigma**2 + 1) ** 0.5)
         return sample






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		int  ,				a		:		Union[str, torch.device] = None  ,				a		:		Optional[int] = None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  num_inference_steps

         __lowerCamelCase		=  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":
             __lowerCamelCase		=  np.linspace(0  ,				num_train_timesteps - 1  ,				a  ,				dtype=a    )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             __lowerCamelCase		=  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
             __lowerCamelCase		=  (np.arange(0  ,				a    ) * step_ratio).round()[::-1].copy().astype(a    )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             __lowerCamelCase		=  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
             __lowerCamelCase		=  (np.arange(a  ,				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'."""    )

         __lowerCamelCase		=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
         __lowerCamelCase		=  np.log(a    )
         __lowerCamelCase		=  np.interp(a  ,				np.arange(0  ,				len(a    )    )  ,				a    )

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

         __lowerCamelCase		=  np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
         __lowerCamelCase		=  torch.from_numpy(a    ).to(device=a    )
         __lowerCamelCase		=  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

         __lowerCamelCase		=  torch.from_numpy(a    )
         __lowerCamelCase		=  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

         if str(a    ).startswith('''mps'''    ):
             # mps does not support float64
             __lowerCamelCase		=  timesteps.to(a  ,				dtype=torch.floataa    )
         else:
             __lowerCamelCase		=  timesteps.to(device=a    )

         # empty dt and derivative
         __lowerCamelCase		=  None
         __lowerCamelCase		=  None

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Any  ,				a		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  np.log(a    )

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

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

         __lowerCamelCase		=  log_sigmas[low_idx]
         __lowerCamelCase		=  log_sigmas[high_idx]

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

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		torch.FloatTensor  ,				a		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  in_sigmas[-1].item()
         __lowerCamelCase		=  in_sigmas[0].item()

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






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         return self.dt is None






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		Union[torch.FloatTensor, np.ndarray]  ,				a		:		Union[float, torch.FloatTensor]  ,				a		:		Union[torch.FloatTensor, np.ndarray]  ,				a		:		bool = True  ,				):





         """simple docstring"""



         __lowerCamelCase		=  self.index_for_timestep(a    )

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

         if self.state_in_first_order:
             __lowerCamelCase		=  self.sigmas[step_index]
             __lowerCamelCase		=  self.sigmas[step_index + 1]
         else:
             # 2nd order / Heun's method
             __lowerCamelCase		=  self.sigmas[step_index - 1]
             __lowerCamelCase		=  self.sigmas[step_index]

         # 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
         __lowerCamelCase		=  0
         __lowerCamelCase		=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             __lowerCamelCase		=  sigma_hat if self.state_in_first_order else sigma_next
             __lowerCamelCase		=  sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             __lowerCamelCase		=  sigma_hat if self.state_in_first_order else sigma_next
             __lowerCamelCase		=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             __lowerCamelCase		=  model_output
         else:
             raise ValueError(
                 f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

         if self.config.clip_sample:
             __lowerCamelCase		=  pred_original_sample.clamp(
                 -self.config.clip_sample_range  ,				self.config.clip_sample_range    )

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

             # store for 2nd order step
             __lowerCamelCase		=  derivative
             __lowerCamelCase		=  dt
             __lowerCamelCase		=  sample
         else:
             # 2. 2nd order / Heun's method
             __lowerCamelCase		=  (sample - pred_original_sample) / sigma_next
             __lowerCamelCase		=  (self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             __lowerCamelCase		=  self.dt
             __lowerCamelCase		=  self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             __lowerCamelCase		=  None
             __lowerCamelCase		=  None
             __lowerCamelCase		=  None

         __lowerCamelCase		=  sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		torch.FloatTensor  ,				a		:		torch.FloatTensor  ,				a		:		torch.FloatTensor  ,				):





         """simple docstring"""



         __lowerCamelCase		=  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
             __lowerCamelCase		=  self.timesteps.to(original_samples.device  ,				dtype=torch.floataa    )
             __lowerCamelCase		=  timesteps.to(original_samples.device  ,				dtype=torch.floataa    )
         else:
             __lowerCamelCase		=  self.timesteps.to(original_samples.device    )
             __lowerCamelCase		=  timesteps.to(original_samples.device    )

         __lowerCamelCase		=  [self.index_for_timestep(a  ,				a    ) for t in timesteps]

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

         __lowerCamelCase		=  original_samples + noise * sigma
         return noisy_samples






     def __len__( self		:		Tuple    ):





         """simple docstring"""



         return self.config.num_train_timesteps




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   int  						=	logging.get_logger(__name__)

lowerCamelCase							:   List[Any]  						=	{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							"""realm"""



      def __init__(self    :    str   ,						UpperCamelCase    :    List[Any]=30522   ,						UpperCamelCase    :    List[Any]=768   ,						UpperCamelCase    :    int=128   ,						UpperCamelCase    :    Any=12   ,						UpperCamelCase    :    Tuple=12   ,						UpperCamelCase    :    List[Any]=8   ,						UpperCamelCase    :    Union[str, Any]=3072   ,						UpperCamelCase    :    List[str]="gelu_new"   ,						UpperCamelCase    :    Any=0.1   ,						UpperCamelCase    :    List[str]=0.1   ,						UpperCamelCase    :    Dict=512   ,						UpperCamelCase    :    Dict=2   ,						UpperCamelCase    :    List[Any]=0.02   ,						UpperCamelCase    :    List[Any]=1E-12   ,						UpperCamelCase    :    Dict=256   ,						UpperCamelCase    :    Union[str, Any]=10   ,						UpperCamelCase    :    Optional[int]=1E-3   ,						UpperCamelCase    :    Tuple=5   ,						UpperCamelCase    :    Optional[int]=320   ,						UpperCamelCase    :    List[str]=13353718   ,						UpperCamelCase    :    Optional[Any]=5000   ,						UpperCamelCase    :    str=1   ,						UpperCamelCase    :    Union[str, Any]=0   ,						UpperCamelCase    :    List[Any]=2   ,						**UpperCamelCase    :    int   ,						):

             '''simple docstring'''







             super().__init__(pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						**UpperCamelCase )

             # Common config
             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= hidden_size
             lowercase__      			= retriever_proj_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= num_candidates
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_act
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= initializer_range
             lowercase__      			= type_vocab_size
             lowercase__      			= layer_norm_eps

             # Reader config
             lowercase__      			= span_hidden_size
             lowercase__      			= max_span_width
             lowercase__      			= reader_layer_norm_eps
             lowercase__      			= reader_beam_size
             lowercase__      			= reader_seq_len

             # Retrieval config
             lowercase__      			= num_block_records
             lowercase__      			= searcher_beam_size




