





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    Optional[Any]=1024   )   ->			Optional[int]:
    UpperCamelCase__							:       Optional[int]      =   [], []

    UpperCamelCase__							:       Union[str, Any]      =   list(zip(__UpperCAmelCase	,     __UpperCAmelCase   )   )
    UpperCamelCase__							:       Union[str, Any]      =   sorted_examples[0]

    def is_too_big(__UpperCAmelCase:    int   ):
        return tok(__UpperCAmelCase	,     return_tensors='''pt'''   ).input_ids.shape[1] > max_tokens

    for src, tgt in tqdm(sorted_examples[1:]   ):
        UpperCamelCase__							:       str      =   new_src + ''' ''' + src
        UpperCamelCase__							:       Tuple      =   new_tgt + ''' ''' + tgt
        if is_too_big(__UpperCAmelCase   ) or is_too_big(__UpperCAmelCase   ):  # cant fit, finalize example
            finished_src.append(__UpperCAmelCase   )
            finished_tgt.append(__UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   src, tgt
        else:  # can fit, keep adding
            UpperCamelCase__							:       Dict      =   cand_src, cand_tgt

    # cleanup
    if new_src:
        assert new_tgt
        finished_src.append(__UpperCAmelCase   )
        finished_tgt.append(__UpperCAmelCase   )
    return finished_src, finished_tgt




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       Any      =   Path(__UpperCAmelCase   )
    save_path.mkdir(exist_ok=__UpperCAmelCase   )
    for split in ["train"]:
        UpperCamelCase__							:       List[str]      =   data_dir / f"{split}.source", data_dir / f"{split}.target"
        UpperCamelCase__							:       Optional[int]      =   [x.rstrip() for x in Path(__UpperCAmelCase   ).open().readlines()]
        UpperCamelCase__							:       int      =   [x.rstrip() for x in Path(__UpperCAmelCase   ).open().readlines()]
        UpperCamelCase__							:       List[Any]      =   pack_examples(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
        print(f"packed {split} split from {len(__UpperCAmelCase   )} examples -> {len(__UpperCAmelCase   )}."   )
        Path(save_path / f"{split}.source"   ).open('''w'''   ).write('''\n'''.join(__UpperCAmelCase   )   )
        Path(save_path / f"{split}.target"   ).open('''w'''   ).write('''\n'''.join(__UpperCAmelCase   )   )
    for split in ["val", "test"]:
        UpperCamelCase__							:       Optional[int]      =   data_dir / f"{split}.source", data_dir / f"{split}.target"
        shutil.copyfile(__UpperCAmelCase	,     save_path / f"{split}.source"   )
        shutil.copyfile(__UpperCAmelCase	,     save_path / f"{split}.target"   )




def 	lowerCAmelCase_					(						)   ->			Optional[Any]:
    UpperCamelCase__							:       Tuple      =   argparse.ArgumentParser()
    parser.add_argument('''--tok_name'''	,     type=__UpperCAmelCase	,     help='''like facebook/bart-large-cnn,t5-base, etc.'''   )
    parser.add_argument('''--max_seq_len'''	,     type=__UpperCAmelCase	,     default=128   )
    parser.add_argument('''--data_dir'''	,     type=__UpperCAmelCase   )
    parser.add_argument('''--save_path'''	,     type=__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   parser.parse_args()
    UpperCamelCase__							:       Optional[int]      =   AutoTokenizer.from_pretrained(args.tok_name   )
    return pack_data_dir(__UpperCAmelCase	,     Path(args.data_dir   )	,     args.max_seq_len	,     args.save_path   )


if __name__ == "__main__":
   packer_cli()








# 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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = 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'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (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
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (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
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = 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
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

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

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




   def __len__(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return self.config.num_train_timesteps

