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


lowerCAmelCase__      		=					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							a_					(		_a		):






 '''simple docstring'''






 UpperCAmelCase_      					=   42
 UpperCAmelCase_      					=   None






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__=0.9_9_9      ,    lowerCAmelCase__="cosine"      ,    ):






       if alpha_transform_type == "cosine":

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

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

       lowerCAmelCase__	      =   []
       for i in range(lowerCAmelCase_      ):
              lowerCAmelCase__	      =   i / num_diffusion_timesteps
              lowerCAmelCase__	      =   (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_					(		_a			,	_a		):






 '''simple docstring'''






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




 @register_to_config
 def __init__(     self   :    List[str]     ,       lowercase__   :    Optional[Any] = 1_000     ,       lowercase__   :    List[Any] = 0.0_001     ,       lowercase__   :    str = 0.02     ,       lowercase__   :    Optional[Any] = "linear"     ,       lowercase__   :    Dict = None     ,       lowercase__   :    Union[str, Any] = "epsilon"     ,       lowercase__   :    Union[str, Any] = "linear"     ,       lowercase__   :    List[str] = False     ,       lowercase__   :    List[str] = "linspace"     ,       lowercase__   :    int = 0     ,       ):







        '''simple docstring'''



        if trained_betas is not None:
               lowerCAmelCase__	      =   torch.tensor(lowerCAmelCase__     ,       dtype=torch.floataa)
        elif beta_schedule == "linear":
               lowerCAmelCase__	      =   torch.linspace(lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__     ,       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     ,       lowerCAmelCase__     ,       dtype=torch.floataa) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowerCAmelCase__	      =   betas_for_alpha_bar(lowerCAmelCase__)
        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)

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

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




 @property
 def 						__snake_case		(     self   :    int):







        '''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   :    str     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



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

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

        lowerCAmelCase__	      =   True
        return sample




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[str] = None):







        '''simple docstring'''



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

        if self.config.interpolation_type == "linear":
               lowerCAmelCase__	      =   np.interp(lowerCAmelCase__     ,       np.arange(0     ,       len(lowerCAmelCase__))     ,       lowerCAmelCase__)
        elif self.config.interpolation_type == "log_linear":
               lowerCAmelCase__	      =   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:
               lowerCAmelCase__	      =   self._convert_to_karras(in_sigmas=lowerCAmelCase__     ,       num_inference_steps=self.num_inference_steps)
               lowerCAmelCase__	      =   np.array([self._sigma_to_t(lowerCAmelCase__     ,       lowerCAmelCase__) for sigma in sigmas])

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




 def 						__snake_case		(     self   :    List[str]     ,       lowercase__   :    Any     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   np.log(lowerCAmelCase__)

        # 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(lowerCAmelCase__     ,       0     ,       1)

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




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    str):







        '''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     ,       lowerCAmelCase__)
        lowerCAmelCase__	      =   sigma_min ** (1 / rho)
        lowerCAmelCase__	      =   sigma_max ** (1 / rho)
        lowerCAmelCase__	      =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    List[Any]     ,       lowercase__   :    int     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    str = 0.0     ,       lowercase__   :    Optional[int] = 0.0     ,       lowercase__   :    Any = float('inf')     ,       lowercase__   :    Union[str, Any] = 1.0     ,       lowercase__   :    Optional[Any] = None     ,       lowercase__   :    Optional[Any] = True     ,       ):







        '''simple docstring'''



        if (
            isinstance(lowerCAmelCase__     ,       lowerCAmelCase__)
            or isinstance(lowerCAmelCase__     ,       torch.IntTensor)
            or isinstance(lowerCAmelCase__     ,       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(lowerCAmelCase__     ,       torch.Tensor):
               lowerCAmelCase__	      =   timestep.to(self.timesteps.device)

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

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

        lowerCAmelCase__	      =   randn_tensor(
            model_output.shape     ,       dtype=model_output.dtype     ,       device=model_output.device     ,       generator=lowerCAmelCase__)

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

        if gamma > 0:
               lowerCAmelCase__	      =   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":
               lowerCAmelCase__	      =   model_output
        elif self.config.prediction_type == "epsilon":
               lowerCAmelCase__	      =   sample - sigma_hat * model_output
        elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowerCAmelCase__	      =   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
        lowerCAmelCase__	      =   (sample - pred_original_sample) / sigma_hat

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

        lowerCAmelCase__	      =   sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__     ,       pred_original_sample=lowerCAmelCase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Any     ,       lowercase__   :    Tuple     ,       lowercase__   :    Optional[Any]     ,       ):







        '''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(lowerCAmelCase__):
               # 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__	      =   [(schedule_timesteps == t).nonzero().item() 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   :    str):







        '''simple docstring'''



        return self.config.num_train_timesteps

import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCAmelCase_    		=				collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    CheckpointState:




       '''simple docstring'''






       print(F"Reading saved model from {model_file}" )
       _A=						torch.load(lowerCAmelCase_							,       map_location=lambda lowerCAmelCase_							,       lowerCAmelCase_ : default_restore_location(lowerCAmelCase_							,       'cpu' ) )
       return CheckpointState(**lowerCAmelCase_ )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						src_file



     def       a__	(       self							):
            raise NotImplementedError





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   *lowerCAmelCase__					,   **lowerCAmelCase__							):
            if comp_type.startswith('c'							):
                   return DPRContextEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('q'							):
                   return DPRQuestionEncoderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            if comp_type.startswith('r'							):
                   return DPRReaderState(*lowerCAmelCase__					,   **lowerCAmelCase__							)
            else:
                   raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'							)





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.ctx_encoder, 'ctx_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
            for key, value in saved_state.model_dict.items():
                   if key.startswith(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.question_encoder, 'question_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
            for key, value in saved_state.model_dict.items():
                   if key.startswith(lowerCAmelCase__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR reader from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{
                'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
            }
            for key, value in saved_state.model_dict.items():
                   if key.startswith('encoder.'							) and not key.startswith('encoder.encode_proj'							):
                          _A=						'encoder.bert_model.' + key[len('encoder.'							) :]
                   _A=						value
            model.span_predictor.load_state_dict(lowerCAmelCase__							)
            return model


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						Path(lowerCAmelCase_ )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_ )

       _A=						DPRState.from_type(lowerCAmelCase_							,       src_file=lowerCAmelCase_ )
       _A=						dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_ )
       model.from_pretrained(lowerCAmelCase_ )  # sanity check


if __name__ == "__main__":
      UpperCAmelCase_    		=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
      )
      parser.add_argument(
          '''--src''',
          type=str,
          help=(
              '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
              ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
              ''' \'retriever\' checkpoints.'''
          ),
      )
      parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				Path(args.src)
      UpperCAmelCase_    		=				F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCAmelCase_    		=				Path(dest_dir)
      assert src_file.exists()
      assert (
          args.type is not None
      ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
      convert(args.type, src_file, dest_dir)