

'''simple docstring'''
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				:   Any      				=  logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							A__					( A__						):
  A__					=       42
  A__					=       None


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  Optional[int]=0.9_99							,				_UpperCamelCase		:  Any="cosine"							,				)   ->    Dict:
     """simple docstring"""




     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_UpperCamelCase		:  str       ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_UpperCamelCase		:  Any       ):
               return math.exp(t * -12.0       )

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

     _SCREAMING_SNAKE_CASE												=[]
     for i in range(_UpperCamelCase       ):
          _SCREAMING_SNAKE_CASE												=i / num_diffusion_timesteps
          _SCREAMING_SNAKE_CASE												=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_UpperCamelCase       ) / alpha_bar_fn(_UpperCamelCase       )							,				_UpperCamelCase       )       )
     return torch.tensor(_UpperCamelCase							,				dtype=torch.floataa       )







class 							A__					( A__  ,     A__						):
  A__					=       [e.name for e in KarrasDiffusionSchedulers]
  A__					=       1





  @register_to_config
  def __init__(	self   :			Union[str, Any]							,      _a   :			int = 1000							,      _a   :			float = 0.00_01							,      _a   :			float = 0.02							,      _a   :			str = "linear"							,      _a   :			Optional[Union[np.ndarray, List[float]]] = None							,      _a   :			str = "epsilon"							,      _a   :			str = "linear"							,      _a   :			Optional[bool] = False							,      _a   :			str = "linspace"							,      _a   :			int = 0							,      )			->					Tuple:





       '''simple docstring'''






       if trained_betas is not None:
            _SCREAMING_SNAKE_CASE												=torch.tensor(_a							,      dtype=torch.floataa				)
       elif beta_schedule == "linear":
            _SCREAMING_SNAKE_CASE												=torch.linspace(_a							,      _a							,      _a							,      dtype=torch.floataa				)
       elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            _SCREAMING_SNAKE_CASE												=(
                torch.linspace(beta_start**0.5							,      beta_end**0.5							,      _a							,      dtype=torch.floataa				) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            _SCREAMING_SNAKE_CASE												=betas_for_alpha_bar(_a				)
       else:
            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"				)

       _SCREAMING_SNAKE_CASE												=1.0 - self.betas
       _SCREAMING_SNAKE_CASE												=torch.cumprod(self.alphas							,      dim=0				)

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

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





  @property
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5





  def 		A						(	self   :			Optional[int]							,      _a   :			torch.FloatTensor							,      _a   :			Union[float, torch.FloatTensor]				)			->					torch.FloatTensor:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE												=True
       return sample





  def 		A						(	self   :			Tuple							,      _a   :			int							,      _a   :			Union[str, torch.device] = None				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=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":
            _SCREAMING_SNAKE_CASE												=np.linspace(0							,      self.config.num_train_timesteps - 1							,      _a							,      dtype=_a				)[
                ::-1
            ].copy()
       elif self.config.timestep_spacing == "leading":
            _SCREAMING_SNAKE_CASE												=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
            _SCREAMING_SNAKE_CASE												=(np.arange(0							,      _a				) * step_ratio).round()[::-1].copy().astype(_a				)
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            _SCREAMING_SNAKE_CASE												=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
            _SCREAMING_SNAKE_CASE												=(np.arange(self.config.num_train_timesteps							,      0							,      -step_ratio				)).round().copy().astype(_a				)
            timesteps -= 1
       else:
            raise ValueError(
                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."				)

       _SCREAMING_SNAKE_CASE												=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
       _SCREAMING_SNAKE_CASE												=np.log(_a				)

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

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





  def 		A						(	self   :			Tuple							,      _a   :			str							,      _a   :			List[Any]				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=np.log(_a				)

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

       # get sigmas range
       _SCREAMING_SNAKE_CASE												=np.cumsum((dists >= 0)							,      axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
       _SCREAMING_SNAKE_CASE												=low_idx + 1

       _SCREAMING_SNAKE_CASE												=log_sigmas[low_idx]
       _SCREAMING_SNAKE_CASE												=log_sigmas[high_idx]

       # interpolate sigmas
       _SCREAMING_SNAKE_CASE												=(low - log_sigma) / (low - high)
       _SCREAMING_SNAKE_CASE												=np.clip(_a							,      0							,      1				)

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





  def 		A						(	self   :			Tuple							,      _a   :			torch.FloatTensor							,      _a   :			Union[str, Any]				)			->					torch.FloatTensor:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=in_sigmas[-1].item()
       _SCREAMING_SNAKE_CASE												=in_sigmas[0].item()

       _SCREAMING_SNAKE_CASE												=7.0  # 7.0 is the value used in the paper
       _SCREAMING_SNAKE_CASE												=np.linspace(0							,      1							,      _a				)
       _SCREAMING_SNAKE_CASE												=sigma_min ** (1 / rho)
       _SCREAMING_SNAKE_CASE												=sigma_max ** (1 / rho)
       _SCREAMING_SNAKE_CASE												=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas





  def 		A						(	self   :			Optional[Any]							,      _a   :			torch.FloatTensor							,      _a   :			Union[float, torch.FloatTensor]							,      _a   :			torch.FloatTensor							,      _a   :			float = 0.0							,      _a   :			float = 0.0							,      _a   :			float = float('inf'				)							,      _a   :			float = 1.0							,      _a   :			Optional[torch.Generator] = None							,      _a   :			bool = True							,      )			->					Union[EulerDiscreteSchedulerOutput, Tuple]:





       '''simple docstring'''






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

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

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

       _SCREAMING_SNAKE_CASE												=randn_tensor(
           model_output.shape							,      dtype=model_output.dtype							,      device=model_output.device							,      generator=_a				)

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

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

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

       _SCREAMING_SNAKE_CASE												=sample + derivative * dt

       if not return_dict:
            return (prev_sample,)

       return EulerDiscreteSchedulerOutput(prev_sample=_a							,      pred_original_sample=_a				)





  def 		A						(	self   :			Dict							,      _a   :			torch.FloatTensor							,      _a   :			torch.FloatTensor							,      _a   :			torch.FloatTensor							,      )			->					torch.FloatTensor:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.sigmas.to(device=original_samples.device							,      dtype=original_samples.dtype				)
       if original_samples.device.type == "mps" and torch.is_floating_point(_a				):
            # mps does not support float64
            _SCREAMING_SNAKE_CASE												=self.timesteps.to(original_samples.device							,      dtype=torch.floataa				)
            _SCREAMING_SNAKE_CASE												=timesteps.to(original_samples.device							,      dtype=torch.floataa				)
       else:
            _SCREAMING_SNAKE_CASE												=self.timesteps.to(original_samples.device				)
            _SCREAMING_SNAKE_CASE												=timesteps.to(original_samples.device				)

       _SCREAMING_SNAKE_CASE												=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       _SCREAMING_SNAKE_CASE												=sigmas[step_indices].flatten()
       while len(sigma.shape				) < len(original_samples.shape				):
            _SCREAMING_SNAKE_CASE												=sigma.unsqueeze(-1				)

       _SCREAMING_SNAKE_CASE												=original_samples + noise * sigma
       return noisy_samples





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





       '''simple docstring'''






       return self.config.num_train_timesteps



'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase				:   int      				=  logging.getLogger(__name__)







@dataclass
class 							A__					:
  A__					=       field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
  A__					=       field(default=A__  ,     metadata={'help': 'Set this flag to use fast tokenization.'}						)
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  A__					=       field(
      default=A__  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,     )


def  _lowerCAmelCase      (		)   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

     if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir       )
         and os.listdir(training_args.output_dir       )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              ' --overwrite_output_dir to overcome.'       )

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,				)
     logger.warning(
         'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,				training_args.local_rank							,				training_args.device							,				training_args.n_gpu							,				bool(training_args.local_rank != -1       )							,				training_args.fpaa							,				)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'							,				_UpperCamelCase       )

     # Prepare Question-Answering task
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _SCREAMING_SNAKE_CASE												=AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)
     _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				use_fast=_UpperCamelCase							,				)
     _SCREAMING_SNAKE_CASE												=AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path							,				from_tf=bool('.ckpt' in model_args.model_name_or_path       )							,				config=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)

     # Get datasets
     _SCREAMING_SNAKE_CASE												=hasattr(model.config							,				'lang2id'       )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir       )
         if training_args.do_train
         else None
     )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				mode='dev'							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)
         if training_args.do_eval
         else None
     )

     # Data collator
     _SCREAMING_SNAKE_CASE												=DataCollatorWithPadding(_UpperCamelCase							,				pad_to_multiple_of=8       ) if training_args.fpaa else None

     # Initialize our Trainer
     _SCREAMING_SNAKE_CASE												=Trainer(
         model=_UpperCamelCase							,				args=_UpperCamelCase							,				train_dataset=_UpperCamelCase							,				eval_dataset=_UpperCamelCase							,				data_collator=_UpperCamelCase							,				)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
