"""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    snake_case     (    __UpperCamelCase  ):
				SCREAMING_SNAKE_CASE_       :				torch.FloatTensor
				SCREAMING_SNAKE_CASE_       :				Optional[torch.FloatTensor]       				=  None






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=0.999 ,			__SCREAMING_SNAKE_CASE="cosine" ,			)     ->     Optional[Any]:
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		__lowerCAmelCase:	Optional[Any]   				=      []
		for i in range(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	int   				=      i / num_diffusion_timesteps
				__lowerCAmelCase:	Dict   				=      (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE			) / alpha_bar_fn(__SCREAMING_SNAKE_CASE			) ,			__SCREAMING_SNAKE_CASE			)			)
		return torch.tensor(__SCREAMING_SNAKE_CASE ,			dtype=torch.floataa			)





class    snake_case     (    __UpperCamelCase,							__UpperCamelCase  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  1

				@register_to_config
				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    int = 1_0_0_0					,  UpperCamelCase__							:    float = 0.0001					,  UpperCamelCase__							:    float = 0.02					,  UpperCamelCase__							:    str = "linear"					,  UpperCamelCase__							:    Optional[Union[np.ndarray, List[float]]] = None					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    int = 0					,  UpperCamelCase__							:    str = "epsilon"					,  UpperCamelCase__							:    float = 1.0					,  **UpperCamelCase__							:    List[Any]					,  )->       Dict:



						'''simple docstring'''


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

						__lowerCAmelCase:	Any   				=      1.0 - self.betas
						__lowerCAmelCase:	List[Any]   				=      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.
						__lowerCAmelCase:	int   				=      torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						__lowerCAmelCase:	Dict   				=      1.0

						# setable values
						__lowerCAmelCase:	List[str]   				=      None
						__lowerCAmelCase:	str   				=      torch.from_numpy(np.arange(0					,  _lowerCAmelCase).copy().astype(np.intaa))

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    torch.FloatTensor					,  UpperCamelCase__							:    Optional[int] = None)->       List[Any]:



						'''simple docstring'''


						return sample

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Union[str, torch.device] = None)->       Optional[Any]:



						'''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.")

						__lowerCAmelCase:	Any   				=      num_inference_steps
						__lowerCAmelCase:	Any   				=      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
						__lowerCAmelCase:	str   				=      (np.arange(0					,  _lowerCAmelCase) * step_ratio).round().copy().astype(np.intaa)
						__lowerCAmelCase:	Optional[int]   				=      torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)
						self.timesteps += self.config.steps_offset

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    torch.FloatTensor					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    torch.FloatTensor					,  UpperCamelCase__							:    float = 0.0					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[torch.FloatTensor] = None					,  UpperCamelCase__							:    bool = True					,  )->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      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
						__lowerCAmelCase:	Any   				=      self.alphas_cumprod[timestep]
						__lowerCAmelCase:	Union[str, Any]   				=      (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						__lowerCAmelCase:	Optional[int]   				=      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":
								__lowerCAmelCase:	List[str]   				=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								__lowerCAmelCase:	int   				=      model_output
						elif self.config.prediction_type == "sample":
								__lowerCAmelCase:	Any   				=      model_output
								__lowerCAmelCase:	List[Any]   				=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
								__lowerCAmelCase:	List[str]   				=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								__lowerCAmelCase:	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:
								__lowerCAmelCase:	Tuple   				=      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
						__lowerCAmelCase:	int   				=      (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
						__lowerCAmelCase:	Tuple   				=      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=_lowerCAmelCase					,  pred_original_sample=_lowerCAmelCase)




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



						'''simple docstring'''


						return self.config.num_train_timesteps





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:	Optional[int]   				=      model
						__lowerCAmelCase:	Tuple   				=      2
						__lowerCAmelCase:	List[Any]   				=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)


