





# 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__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    torch.FloatTensor
   __A       :    torch.FloatTensor





class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Any	  =  1

   @register_to_config
   def __init__( self ,  lowercase = 2000 ,  lowercase = 0.15 ,  lowercase = 0.01 ,  lowercase = 1348.0 ,  lowercase = 1e-5 ,  lowercase = 1 ,  )      ->						Dict:







          '''simple docstring'''

          a__						:     Optional[int]			=    sigma_max

          # setable values
          a__						:     Any			=    None

          self.set_sigmas(lowercase ,  lowercase ,  lowercase ,  lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						torch.FloatTensor:







          '''simple docstring'''

          return sample

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = None)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

          a__						:     List[str]			=    torch.linspace(1 ,  lowercase ,  lowercase ,  device=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = None ,  lowercase = None)      ->						str:







          '''simple docstring'''

          a__						:     Tuple			=    sigma_min if sigma_min is not None else self.config.sigma_min
          a__						:     Optional[Any]			=    sigma_max if sigma_max is not None else self.config.sigma_max
          a__						:     Optional[Any]			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                 self.set_timesteps(lowercase ,  lowercase)

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

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Dict:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = None ,  lowercase = 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')

          a__						:     str			=    timestep * torch.ones(
              sample.shape[0] ,  device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
          a__						:     Optional[int]			=    (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
          a__						:     int			=    timesteps.to(self.discrete_sigmas.device)

          a__						:     List[str]			=    self.discrete_sigmas[timesteps].to(sample.device)
          a__						:     Dict			=    self.get_adjacent_sigma(lowercase ,  lowercase).to(sample.device)
          a__						:     Tuple			=    torch.zeros_like(lowercase)
          a__						:     Tuple			=    (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
          a__						:     int			=    diffusion.flatten()
          while len(diffusion.shape) < len(sample.shape):
                 a__						:     List[Any]			=    diffusion.unsqueeze(-1)
          a__						:     List[str]			=    drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          a__						:     List[Any]			=    randn_tensor(
              sample.shape ,  layout=sample.layout ,  generator=lowercase ,  device=sample.device ,  dtype=sample.dtype)
          a__						:     Optional[Any]			=    sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          a__						:     Optional[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=lowercase ,  prev_sample_mean=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = None ,  lowercase = 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
          a__						:     Optional[Any]			=    randn_tensor(sample.shape ,  layout=sample.layout ,  generator=lowercase).to(sample.device)

          # compute step size from the model_output, the noise, and the snr
          a__						:     Union[str, Any]			=    torch.norm(model_output.reshape(model_output.shape[0] ,  -1) ,  dim=-1).mean()
          a__						:     Optional[int]			=    torch.norm(noise.reshape(noise.shape[0] ,  -1) ,  dim=-1).mean()
          a__						:     Union[str, Any]			=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          a__						:     List[str]			=    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
          a__						:     List[Any]			=    step_size.flatten()
          while len(step_size.shape) < len(sample.shape):
                 a__						:     Optional[int]			=    step_size.unsqueeze(-1)
          a__						:     Union[str, Any]			=    sample + step_size * model_output
          a__						:     List[str]			=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  )      ->						torch.FloatTensor:







          '''simple docstring'''

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

   def __len__( self)      ->						Optional[Any]:







          '''simple docstring'''

          return self.config.num_train_timesteps









import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def A_     (		A__		,				A__		,				A__    )  ->		Any:
       # Construct model
       if gpta_config_file == "":
              a__						:     Optional[int]			=    GPTaConfig()
       else:
              a__						:     List[str]			=    GPTaConfig.from_json_file(A__    )
       a__						:     List[str]			=    GPTaModel(A__    )

       # Load weights from numpy
       load_tf_weights_in_gpta(A__		,				A__		,				A__    )

       # Save pytorch-model
       a__						:     Optional[Any]			=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       a__						:     Any			=    pytorch_dump_folder_path + '/' + CONFIG_NAME
       print(F'Save PyTorch model to {pytorch_weights_dump_path}'    )
       torch.save(model.state_dict()		,				A__    )
       print(F'Save configuration file to {pytorch_config_dump_path}'    )
       with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
              f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	Dict      						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


