
"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		42
       UpperCamelCase_ =		None




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=0.999			,					_UpperCamelCase="cosine"			,					):






  """simple docstring"""



  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_UpperCamelCase    ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

  elif alpha_transform_type == "exp":

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

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

  _lowercase:      Any   			=  []
  for i in range(_UpperCamelCase    ):
    _lowercase:      List[str]   			=  i / num_diffusion_timesteps
    _lowercase:      Optional[int]   			=  (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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		1
       @register_to_config
       def __init__(  self			,					A_ = 1000			,					A_ = 0.00_01			,					A_ = 0.02			,					A_ = "linear"			,					A_ = None			,					A_ = True			,					A_ = True			,					A_ = 0			,					A_ = "epsilon"			,					A_ = 1.0			,					**A_			,					)  ->						Any:





         """simple docstring"""




         if kwargs.get('''set_alpha_to_one'''			,					A_							) is not None:
           _lowercase:      Dict   			=  (
               '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
           )
           deprecate('''set_alpha_to_one'''			,					'''1.0.0'''			,					A_			,					standard_warn=A_							)
           _lowercase:      int   			=  kwargs['''set_alpha_to_one''']
         if trained_betas is not None:
           _lowercase:      int   			=  torch.tensor(A_			,					dtype=torch.floataa							)
         elif beta_schedule == "linear":
           _lowercase:      str   			=  torch.linspace(A_			,					A_			,					A_			,					dtype=torch.floataa							)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowercase:      Optional[Any]   			=  (
               torch.linspace(beta_start**0.5			,					beta_end**0.5			,					A_			,					dtype=torch.floataa							) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowercase:      Tuple   			=  betas_for_alpha_bar(A_							)
         else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''							)

         _lowercase:      List[Any]   			=  1.0 - self.betas
         _lowercase:      List[str]   			=  torch.cumprod(self.alphas			,					dim=0							)

         # At every step in inverted ddim, we are looking into the next alphas_cumprod
         # For the final step, there is no next alphas_cumprod, and the index is out of bounds
         # `set_alpha_to_zero` decides whether we set this parameter simply to zero
         # in this case, self.step() just output the predicted noise
         # or whether we use the final alpha of the "non-previous" one.
         _lowercase:      Optional[int]   			=  torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

         # standard deviation of the initial noise distribution
         _lowercase:      List[str]   			=  1.0

         # setable values
         _lowercase:      Tuple   			=  None
         _lowercase:      Optional[Any]   			=  torch.from_numpy(np.arange(0			,					A_							).copy().astype(np.intaa							)							)
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						torch.FloatTensor:





         """simple docstring"""




         return sample
       def 				lowercase_				(  self			,					A_			,					A_ = None							)  ->						List[str]:





         """simple docstring"""




         if num_inference_steps > self.config.num_train_timesteps:
           raise ValueError(
               f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
               f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
               f''' maximal {self.config.num_train_timesteps} timesteps.'''							)

         _lowercase:      Union[str, Any]   			=  num_inference_steps
         _lowercase:      Dict   			=  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
         _lowercase:      Any   			=  (np.arange(0			,					A_							) * step_ratio).round().copy().astype(np.intaa							)
         _lowercase:      int   			=  torch.from_numpy(A_							).to(A_							)
         self.timesteps += self.config.steps_offset
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_ = 0.0			,					A_ = False			,					A_ = None			,					A_ = True			,					)  ->						Union[DDIMSchedulerOutput, Tuple]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  timestep + self.config.num_train_timesteps // self.num_inference_steps

         # 2. compute alphas, betas
         # change original implementation to exactly match noise levels for analogous forward process
         _lowercase:      Union[str, Any]   			=  self.alphas_cumprod[timestep]
         _lowercase:      Union[str, Any]   			=  (
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             else self.final_alpha_cumprod
         )

         _lowercase:      Optional[Any]   			=  1 - alpha_prod_t

         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
           _lowercase:      List[str]   			=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           _lowercase:      Any   			=  model_output
         elif self.config.prediction_type == "sample":
           _lowercase:      Optional[Any]   			=  model_output
           _lowercase:      Union[str, Any]   			=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
           _lowercase:      Dict   			=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           _lowercase:      Union[str, Any]   			=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
               ''' `v_prediction`'''							)

         # 4. Clip or threshold "predicted x_0"
         if self.config.clip_sample:
           _lowercase:      Any   			=  pred_original_sample.clamp(
               -self.config.clip_sample_range			,					self.config.clip_sample_range							)

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowercase:      Optional[Any]   			=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         _lowercase:      List[Any]   			=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
           return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=A_			,					pred_original_sample=A_							)



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





         """simple docstring"""




         return self.config.num_train_timesteps


"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  BigBirdForPreTraining(_UpperCamelCase    )

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_UpperCamelCase			,					_UpperCamelCase			,					is_trivia_qa=_UpperCamelCase    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       List[Any]				= 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(
      '--big_bird_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.'
  )
  parser.add_argument(
      '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
  )
  A__ :       Optional[Any]				= parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )
