
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput


class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			int        =    1




      @register_to_config
      def __init__(      self		:       Any				,       _snake_case		:       int = 1000				,       _snake_case		:       Optional[Union[np.ndarray, List[float]]] = None				):
          # set `betas`, `alphas`, `timesteps`
          self.set_timesteps(_snake_case				)

          # standard deviation of the initial noise distribution
          __lowercase		:			Optional[int]	    =       1.0

          # For now we only support F-PNDM, i.e. the runge-kutta method
          # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
          # mainly at formula (9), (12), (13) and the Algorithm 2.
          __lowercase		:			Optional[int]	    =       4

          # running values
          __lowercase		:			Any	    =       []




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       int				,       _snake_case		:       Union[str, torch.device] = None				):
          __lowercase		:			Optional[int]	    =       num_inference_steps
          __lowercase		:			Tuple	    =       torch.linspace(1				,       0				,       num_inference_steps + 1				)[:-1]
          __lowercase		:			Union[str, Any]	    =       torch.cat([steps, torch.tensor([0.0]				)]				)

          if self.config.trained_betas is not None:
              __lowercase		:			Optional[int]	    =       torch.tensor(self.config.trained_betas				,       dtype=torch.floataa				)
          else:
              __lowercase		:			str	    =       torch.sin(steps * math.pi / 2				) ** 2

          __lowercase		:			Tuple	    =       (1.0 - self.betas**2) ** 0.5

          __lowercase		:			List[Any]	    =       (torch.atana(self.betas				,       self.alphas				) / math.pi * 2)[:-1]
          __lowercase		:			Optional[Any]	    =       timesteps.to(_snake_case				)

          __lowercase		:			Optional[int]	    =       []




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       torch.FloatTensor				,       _snake_case		:       int				,       _snake_case		:       torch.FloatTensor				,       _snake_case		:       bool = True				,       ):
          if self.num_inference_steps is None:
              raise ValueError(
                  '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''				)

          __lowercase		:			Dict	    =       (self.timesteps == timestep).nonzero().item()
          __lowercase		:			Any	    =       timestep_index + 1

          __lowercase		:			Tuple	    =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
          self.ets.append(_snake_case				)

          if len(self.ets				) == 1:
              __lowercase		:			str	    =       self.ets[-1]
          elif len(self.ets				) == 2:
              __lowercase		:			str	    =       (3 * self.ets[-1] - self.ets[-2]) / 2
          elif len(self.ets				) == 3:
              __lowercase		:			Optional[int]	    =       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
          else:
              __lowercase		:			Tuple	    =       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

          __lowercase		:			List[str]	    =       self._get_prev_sample(_snake_case				,       _snake_case				,       _snake_case				,       _snake_case				)

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=_snake_case				)




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       torch.FloatTensor				,       *_snake_case		:       List[str]				,       **_snake_case		:       Tuple				):
          return sample




      def 		snake_case_			(      self		:       str				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       int				,       _snake_case		:       Dict				,       _snake_case		:       List[str]				):
          __lowercase		:			str	    =       self.alphas[timestep_index]
          __lowercase		:			int	    =       self.betas[timestep_index]

          __lowercase		:			List[str]	    =       self.alphas[prev_timestep_index]
          __lowercase		:			Optional[Any]	    =       self.betas[prev_timestep_index]

          __lowercase		:			Optional[int]	    =       (sample - sigma * ets) / max(_snake_case				,       1E-8				)
          __lowercase		:			Any	    =       next_alpha * pred + ets * next_sigma

          return prev_sample




      def __len__(      self		:       List[Any]				):
          return self.config.num_train_timesteps




import csv

import tweepy

# Twitter API credentials
__lowerCAmelCase					:			str			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Any			      =		""
__lowerCAmelCase					:			Optional[int]			      =		""


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						None:
    # authorize twitter, initialize tweepy
    __lowercase		:			List[Any]	    =       tweepy.OAuthHandler(__lowerCAmelCase	,					__lowerCAmelCase	)
    auth.set_access_token(__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			str	    =       tweepy.API(__lowerCAmelCase	)

    # initialize a list to hold all the tweepy Tweets
    __lowercase		:			Union[str, Any]	    =       []

    # make initial request for most recent tweets (200 is the maximum allowed count)
    __lowercase		:			List[Any]	    =       api.user_timeline(screen_name=__lowerCAmelCase	,					count=200	)

    # save most recent tweets
    alltweets.extend(__lowerCAmelCase	)

    # save the id of the oldest tweet less one
    __lowercase		:			Any	    =       alltweets[-1].id - 1

    # keep grabbing tweets until there are no tweets left to grab
    while len(__lowerCAmelCase	) > 0:
        print(F'getting tweets before {oldest}'	)

        # all subsequent requests use the max_id param to prevent duplicates
        __lowercase		:			Tuple	    =       api.user_timeline(
            screen_name=__lowerCAmelCase	,					count=200	,					max_id=__lowerCAmelCase	)

        # save most recent tweets
        alltweets.extend(__lowerCAmelCase	)

        # update the id of the oldest tweet less one
        __lowercase		:			Any	    =       alltweets[-1].id - 1

        print(F'...{len(__lowerCAmelCase	)} tweets downloaded so far'	)

    # transform the tweepy tweets into a 2D array that will populate the csv
    __lowercase		:			Optional[int]	    =       [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

    # write the csv
    with open(F'new_{screen_name}_tweets.csv'	,					'''w'''	) as f:
        __lowercase		:			int	    =       csv.writer(__lowerCAmelCase	)
        writer.writerow(['''id''', '''created_at''', '''text''']	)
        writer.writerows(__lowerCAmelCase	)


if __name__ == "__main__":
 # pass in the username of the account you want to download
 get_all_tweets("FirePing32")


