




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 		_A   (    A_ ):
  _UpperCamelCase  :     torch.FloatTensor
  _UpperCamelCase  :     torch.FloatTensor


class 		_A   (    A_     ,  A_ ):
  _UpperCamelCase  :     str					=     1






  @register_to_config
  def __init__(    self						:      Optional[int] ,		_A						:      int = 2_000 ,		_A						:      float = 0.15 ,		_A						:      float = 0.01 ,		_A						:      float = 1_348.0 ,		_A						:      float = 1E-5 ,		_A						:      int = 1 ,		)						->       Optional[Any]:






       """simple docstring"""







       lowercase   :  List[Any]        =					sigma_max

       # setable values
       lowercase   :  Optional[Any]        =					None

       self.set_sigmas(snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ )






  def    __a     (    self						:      List[str] ,		_A						:      torch.FloatTensor ,		_A						:      Optional[int] = None )						->       torch.FloatTensor:






       """simple docstring"""







       return sample






  def    __a     (    self						:      Any ,		_A						:      int ,		_A						:      float = None ,		_A						:      Union[str, torch.device] = None )						->       Dict:






       """simple docstring"""







       lowercase   :  Union[str, Any]        =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

       lowercase   :  Tuple        =					torch.linspace(1 ,		snake_case__ ,		snake_case__ ,		device=snake_case__ )






  def    __a     (    self						:      List[Any] ,		_A						:      int ,		_A						:      float = None ,		_A						:      float = None ,		_A						:      float = None )						->       Optional[Any]:






       """simple docstring"""







       lowercase   :  List[str]        =					sigma_min if sigma_min is not None else self.config.sigma_min
       lowercase   :  str        =					sigma_max if sigma_max is not None else self.config.sigma_max
       lowercase   :  Tuple        =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
            self.set_timesteps(snake_case__ ,		snake_case__ )

       lowercase   :  int        =					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       lowercase   :  int        =					torch.exp(torch.linspace(math.log(snake_case__ ) ,		math.log(snake_case__ ) ,		snake_case__ ) )
       lowercase   :  List[str]        =					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )






  def    __a     (    self						:      Optional[int] ,		_A						:      Tuple ,		_A						:      Optional[int] )						->       Any:






       """simple docstring"""







       return torch.where(
           timesteps == 0 ,		torch.zeros_like(t.to(timesteps.device ) ) ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device ) ,		)






  def    __a     (    self						:      int ,		_A						:      torch.FloatTensor ,		_A						:      int ,		_A						:      torch.FloatTensor ,		_A						:      Optional[torch.Generator] = None ,		_A						:      bool = True ,		)						->       Union[SdeVeOutput, Tuple]:






       """simple docstring"""







       if self.timesteps is None:
            raise ValueError(
                '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )

       lowercase   :  Optional[int]        =					timestep * torch.ones(
           sample.shape[0] ,		device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
       lowercase   :  str        =					(timestep * (len(self.timesteps ) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       lowercase   :  Any        =					timesteps.to(self.discrete_sigmas.device )

       lowercase   :  Dict        =					self.discrete_sigmas[timesteps].to(sample.device )
       lowercase   :  Union[str, Any]        =					self.get_adjacent_sigma(snake_case__ ,		snake_case__ ).to(sample.device )
       lowercase   :  Optional[Any]        =					torch.zeros_like(snake_case__ )
       lowercase   :  Dict        =					(sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       lowercase   :  int        =					diffusion.flatten()
       while len(diffusion.shape ) < len(sample.shape ):
            lowercase   :  Any        =					diffusion.unsqueeze(-1 )
       lowercase   :  Tuple        =					drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       lowercase   :  Tuple        =					randn_tensor(
           sample.shape ,		layout=sample.layout ,		generator=snake_case__ ,		device=sample.device ,		dtype=sample.dtype )
       lowercase   :  List[str]        =					sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       lowercase   :  int        =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
            return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=snake_case__ ,		prev_sample_mean=snake_case__ )






  def    __a     (    self						:      Union[str, Any] ,		_A						:      torch.FloatTensor ,		_A						:      torch.FloatTensor ,		_A						:      Optional[torch.Generator] = None ,		_A						:      bool = True ,		)						->       Union[SchedulerOutput, Tuple]:






       """simple docstring"""







       if self.timesteps is None:
            raise ValueError(
                '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       lowercase   :  str        =					randn_tensor(sample.shape ,		layout=sample.layout ,		generator=snake_case__ ).to(sample.device )

       # compute step size from the model_output, the noise, and the snr
       lowercase   :  Union[str, Any]        =					torch.norm(model_output.reshape(model_output.shape[0] ,		-1 ) ,		dim=-1 ).mean()
       lowercase   :  List[Any]        =					torch.norm(noise.reshape(noise.shape[0] ,		-1 ) ,		dim=-1 ).mean()
       lowercase   :  List[Any]        =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       lowercase   :  Optional[Any]        =					step_size * torch.ones(sample.shape[0] ).to(sample.device )
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       lowercase   :  List[Any]        =					step_size.flatten()
       while len(step_size.shape ) < len(sample.shape ):
            lowercase   :  Dict        =					step_size.unsqueeze(-1 )
       lowercase   :  Any        =					sample + step_size * model_output
       lowercase   :  int        =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case__ )






  def    __a     (    self						:      Tuple ,		_A						:      torch.FloatTensor ,		_A						:      torch.FloatTensor ,		_A						:      torch.FloatTensor ,		)						->       torch.FloatTensor:






       """simple docstring"""







       lowercase   :  Union[str, Any]        =					timesteps.to(original_samples.device )
       lowercase   :  int        =					self.discrete_sigmas.to(original_samples.device )[timesteps]
       lowercase   :  Any        =					(
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(snake_case__ ) * sigmas[:, None, None, None]
       )
       lowercase   :  Optional[int]        =					noise + original_samples
       return noisy_samples






  def __len__(    self						:      List[Any] )						->       int:






       """simple docstring"""







       return self.config.num_train_timesteps

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





