import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 						snake_case_ (	__A ):
   __A			:	torch.FloatTensor
   __A			:	Optional[torch.FloatTensor]        =       None



def     lowercase_       (    _lowerCamelCase							: List[str]	,			_lowerCamelCase							: List[Any]=0.999	,			_lowerCamelCase							: List[str]="cosine"	,			):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_lowerCamelCase							: str):
            return math.exp(t * -12.0)

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

    lowercase__				:     int					      =       []
    for i in range(_lowerCamelCase):
        lowercase__				:     List[Any]					      =       i / num_diffusion_timesteps
        lowercase__				:     Dict					      =       (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 						snake_case_ (	__A						,__A ):
   __A			:	Any        =       [e.name for e in KarrasDiffusionSchedulers]
   __A			:	Any        =       1






   @register_to_config
   def __init__(   self       :  str			,		lowercase_       :  int = 10_00			,		lowercase_       :  float = 0.00_01			,		lowercase_       :  float = 0.02			,		lowercase_       :  str = "linear"			,		lowercase_       :  Optional[Union[np.ndarray, List[float]]] = None			,		lowercase_       :  Optional[bool] = False			,		lowercase_       :  str = "epsilon"			,		lowercase_       :  str = "linspace"			,		lowercase_       :  int = 0			,		)      ->		List[Any]:
       if trained_betas is not None:
           lowercase__				:     Any					      =       torch.tensor(lowercase_			,		dtype=torch.floataa						)
       elif beta_schedule == "linear":
           lowercase__				:     Optional[Any]					      =       torch.linspace(lowercase_			,		lowercase_			,		lowercase_			,		dtype=torch.floataa						)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowercase__				:     List[Any]					      =       (
               torch.linspace(beta_start**0.5			,		beta_end**0.5			,		lowercase_			,		dtype=torch.floataa						) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowercase__				:     Optional[int]					      =       betas_for_alpha_bar(lowercase_						)
       else:
           raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

       lowercase__				:     List[Any]					      =       1.0 - self.betas
       lowercase__				:     Dict					      =       torch.cumprod(self.alphas			,		dim=0						)

       lowercase__				:     Any					      =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
       lowercase__				:     Tuple					      =       np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
       lowercase__				:     List[str]					      =       torch.from_numpy(lowercase_						)

       # setable values
       lowercase__				:     Tuple					      =       None
       lowercase__				:     Dict					      =       use_karras_sigmas
       self.set_timesteps(lowercase_			,		lowercase_						)
       lowercase__				:     Optional[int]					      =       []
       lowercase__				:     Dict					      =       False






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Optional[int]:
       # 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    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  torch.FloatTensor			,		lowercase_       :  Union[float, torch.FloatTensor]						)      ->		torch.FloatTensor:
       if isinstance(lowercase_			,		torch.Tensor						):
           lowercase__				:     Dict					      =       timestep.to(self.timesteps.device						)
       lowercase__				:     Optional[int]					      =       (self.timesteps == timestep).nonzero().item()
       lowercase__				:     int					      =       self.sigmas[step_index]
       lowercase__				:     Union[str, Any]					      =       sample / ((sigma**2 + 1) ** 0.5)
       lowercase__				:     Optional[int]					      =       True
       return sample






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  int			,		lowercase_       :  Optional[int]			,		lowercase_       :  Tuple						)      ->		List[Any]:

       def lms_derivative(lowercase_       :  Optional[int]						):
           lowercase__				:     Optional[Any]					      =       1.0
           for k in range(lowercase_						):
               if current_order == k:
                   continue
               prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
           return prod

       lowercase__				:     Optional[Any]					      =       integrate.quad(lowercase_			,		self.sigmas[t]			,		self.sigmas[t + 1]			,		epsrel=1E-4						)[0]

       return integrated_coeff






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  int			,		lowercase_       :  Union[str, torch.device] = None						)      ->		int:
       lowercase__				:     Union[str, Any]					      =       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":
           lowercase__				:     Union[str, Any]					      =       np.linspace(0			,		self.config.num_train_timesteps - 1			,		lowercase_			,		dtype=lowercase_						)[
               ::-1
           ].copy()
       elif self.config.timestep_spacing == "leading":
           lowercase__				:     Optional[int]					      =       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
           lowercase__				:     Optional[int]					      =       (np.arange(0			,		lowercase_						) * step_ratio).round()[::-1].copy().astype(lowercase_						)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           lowercase__				:     Optional[Any]					      =       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
           lowercase__				:     List[Any]					      =       (np.arange(self.config.num_train_timesteps			,		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\'.'''						)

       lowercase__				:     Optional[int]					      =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
       lowercase__				:     List[Any]					      =       np.log(lowercase_						)
       lowercase__				:     str					      =       np.interp(lowercase_			,		np.arange(0			,		len(lowercase_						)						)			,		lowercase_						)

       if self.use_karras_sigmas:
           lowercase__				:     Union[str, Any]					      =       self._convert_to_karras(in_sigmas=lowercase_						)
           lowercase__				:     Optional[Any]					      =       np.array([self._sigma_to_t(lowercase_			,		lowercase_						) for sigma in sigmas]						)

       lowercase__				:     List[str]					      =       np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

       lowercase__				:     Optional[Any]					      =       torch.from_numpy(lowercase_						).to(device=lowercase_						)
       if str(lowercase_						).startswith("mps"						):
           # mps does not support float64
           lowercase__				:     List[str]					      =       torch.from_numpy(lowercase_						).to(lowercase_			,		dtype=torch.floataa						)
       else:
           lowercase__				:     Dict					      =       torch.from_numpy(lowercase_						).to(device=lowercase_						)

       lowercase__				:     Optional[Any]					      =       []






   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  str			,		lowercase_       :  Dict						)      ->		Any:
       # get log sigma
       lowercase__				:     Dict					      =       np.log(lowercase_						)

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

       # get sigmas range
       lowercase__				:     Tuple					      =       np.cumsum((dists >= 0)			,		axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
       lowercase__				:     Union[str, Any]					      =       low_idx + 1

       lowercase__				:     Optional[Any]					      =       log_sigmas[low_idx]
       lowercase__				:     int					      =       log_sigmas[high_idx]

       # interpolate sigmas
       lowercase__				:     int					      =       (low - log_sigma) / (low - high)
       lowercase__				:     Any					      =       np.clip(lowercase_			,		0			,		1						)

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






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  torch.FloatTensor						)      ->		torch.FloatTensor:
       lowercase__				:     float					      =       in_sigmas[-1].item()
       lowercase__				:     float					      =       in_sigmas[0].item()

       lowercase__				:     Any					      =       7.0  # 7.0 is the value used in the paper
       lowercase__				:     Union[str, Any]					      =       np.linspace(0			,		1			,		self.num_inference_steps						)
       lowercase__				:     Optional[Any]					      =       sigma_min ** (1 / rho)
       lowercase__				:     Any					      =       sigma_max ** (1 / rho)
       lowercase__				:     List[str]					      =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  torch.FloatTensor			,		lowercase_       :  Union[float, torch.FloatTensor]			,		lowercase_       :  torch.FloatTensor			,		lowercase_       :  int = 4			,		lowercase_       :  bool = True			,		)      ->		Union[LMSDiscreteSchedulerOutput, Tuple]:
       if not self.is_scale_input_called:
           warnings.warn(
               "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
               "See `StableDiffusionPipeline` for a usage example."						)

       if isinstance(lowercase_			,		torch.Tensor						):
           lowercase__				:     Optional[int]					      =       timestep.to(self.timesteps.device						)
       lowercase__				:     str					      =       (self.timesteps == timestep).nonzero().item()
       lowercase__				:     Tuple					      =       self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           lowercase__				:     Dict					      =       sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase__				:     List[Any]					      =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
           lowercase__				:     List[str]					      =       model_output
       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
       lowercase__				:     List[Any]					      =       (sample - pred_original_sample) / sigma
       self.derivatives.append(lowercase_						)
       if len(self.derivatives						) > order:
           self.derivatives.pop(0						)

       # 3. Compute linear multistep coefficients
       lowercase__				:     str					      =       min(step_index + 1			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       [self.get_lms_coefficient(lowercase_			,		lowercase_			,		lowercase_						) for curr_order in range(lowercase_						)]

       # 4. Compute previous sample based on the derivatives path
       lowercase__				:     Dict					      =       sample + sum(
           coeff * derivative for coeff, derivative in zip(lowercase_			,		reversed(self.derivatives						)						)						)

       if not return_dict:
           return (prev_sample,)

       return LMSDiscreteSchedulerOutput(prev_sample=lowercase_			,		pred_original_sample=lowercase_						)






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  torch.FloatTensor			,		lowercase_       :  torch.FloatTensor			,		lowercase_       :  torch.FloatTensor			,		)      ->		torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       lowercase__				:     Any					      =       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
           lowercase__				:     Union[str, Any]					      =       self.timesteps.to(original_samples.device			,		dtype=torch.floataa						)
           lowercase__				:     Any					      =       timesteps.to(original_samples.device			,		dtype=torch.floataa						)
       else:
           lowercase__				:     List[Any]					      =       self.timesteps.to(original_samples.device						)
           lowercase__				:     Optional[Any]					      =       timesteps.to(original_samples.device						)

       lowercase__				:     int					      =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       lowercase__				:     List[Any]					      =       sigmas[step_indices].flatten()
       while len(sigma.shape						) < len(original_samples.shape						):
           lowercase__				:     List[str]					      =       sigma.unsqueeze(-1						)

       lowercase__				:     int					      =       original_samples + noise * sigma
       return noisy_samples







   def __len__(   self       :  int						)      ->		str:
       return self.config.num_train_timesteps




import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       RetriBertConfig
   __A			:	str        =       None
   __A			:	Any        =       "retribert"






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[Any]						)      ->		List[str]:
       if isinstance(lowercase_			,		nn.Linear						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.bias is not None:
               module.bias.data.zero_()
       elif isinstance(lowercase_			,		nn.Embedding						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.padding_idx is not None:
               module.weight.data[module.padding_idx].zero_()
       elif isinstance(lowercase_			,		nn.LayerNorm						):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0						)



UpperCamelCase													=		R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''



@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."						,__A						,)
class 						snake_case_ (	__A ):






   def __init__(   self       :  Dict			,		lowercase_       :  RetriBertConfig						)      ->		None:
       super().__init__(lowercase_						)
       lowercase__				:     str					      =       config.projection_dim

       lowercase__				:     Optional[int]					      =       BertModel(lowercase_						)
       lowercase__				:     Union[str, Any]					      =       None if config.share_encoders else BertModel(lowercase_						)
       lowercase__				:     Tuple					      =       nn.Dropout(config.hidden_dropout_prob						)
       lowercase__				:     Any					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)
       lowercase__				:     str					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)

       lowercase__				:     Optional[Any]					      =       nn.CrossEntropyLoss(reduction="mean"						)

       # Initialize weights and apply final processing
       self.post_init()






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  str			,		lowercase_       :  Optional[int]			,		lowercase_       :  Tuple			,		lowercase_       :  Optional[int]=-1			,		)      ->		List[str]:
       # reproduces BERT forward pass with checkpointing
       if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
           return sent_encoder(lowercase_			,		attention_mask=lowercase_						)[1]
       else:
           # prepare implicit variables
           lowercase__				:     Union[str, Any]					      =       input_ids.device
           lowercase__				:     Union[str, Any]					      =       input_ids.size()
           lowercase__				:     int					      =       torch.zeros(lowercase_			,		dtype=torch.long			,		device=lowercase_						)
           lowercase__				:     List[Any]					      =       [None] * sent_encoder.config.num_hidden_layers
           lowercase__				:     torch.Tensor					      =       sent_encoder.get_extended_attention_mask(
               lowercase_			,		lowercase_						)

           # define function for checkpointing
           def partial_encode(*lowercase_       :  int						):
               lowercase__				:     List[Any]					      =       sent_encoder.encoder(
                   inputs[0]			,		attention_mask=inputs[1]			,		head_mask=lowercase_			,		)
               lowercase__				:     Dict					      =       encoder_outputs[0]
               lowercase__				:     List[Any]					      =       sent_encoder.pooler(lowercase_						)
               return pooled_output

           # run embedding layer on everything at once
           lowercase__				:     Optional[int]					      =       sent_encoder.embeddings(
               input_ids=lowercase_			,		position_ids=lowercase_			,		token_type_ids=lowercase_			,		inputs_embeds=lowercase_						)
           # run encoding and pooling on one mini-batch at a time
           lowercase__				:     str					      =       []
           for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
               lowercase__				:     int					      =       embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       checkpoint.checkpoint(lowercase_			,		lowercase_			,		lowercase_						)
               pooled_output_list.append(lowercase_						)
           return torch.cat(lowercase_			,		dim=0						)






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Any			,		lowercase_       :  Any=None			,		lowercase_       :  Union[str, Any]=-1			,		)      ->		List[str]:
       lowercase__				:     Optional[Any]					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query			,		lowercase_			,		)
       return self.project_query(lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Dict=-1			,		)      ->		Optional[int]:
       lowercase__				:     str					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query if self.bert_doc is None else self.bert_doc			,		lowercase_			,		)
       return self.project_doc(lowercase_						)







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  int = -1			,		)      ->		torch.FloatTensor:
       lowercase__				:     Tuple					      =       input_ids_query.device
       lowercase__				:     str					      =       self.embed_questions(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     Any					      =       self.embed_answers(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     List[str]					      =       torch.mm(lowercase_			,		a_reps.t()						)
       lowercase__				:     Optional[int]					      =       self.ce_loss(lowercase_			,		torch.arange(compare_scores.shape[1]						).to(lowercase_						)						)
       lowercase__				:     str					      =       self.ce_loss(compare_scores.t()			,		torch.arange(compare_scores.shape[0]						).to(lowercase_						)						)
       lowercase__				:     Tuple					      =       (loss_qa + loss_aq) / 2
       return loss



