"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class 					_A		:
      snake_case__     :  CommonSchedulerState

      # setable values
      snake_case__     :  jnp.ndarray
      snake_case__     :  jnp.ndarray
      snake_case__     :  Optional[int]				    =      None

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return cls(common=__UpperCAmelCase						,			init_noise_sigma=__UpperCAmelCase						,			timesteps=__UpperCAmelCase		)






@dataclass
class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  DDPMSchedulerState






class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase      ):
      snake_case__     :  Union[str, Any]				    =      [e.name for e in FlaxKarrasDiffusionSchedulers]

      snake_case__     :  jnp.dtype

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return True

      @register_to_config
      def __init__(		self						,			__lowerCAmelCase = 1000						,			__lowerCAmelCase = 0.0_0_0_1						,			__lowerCAmelCase = 0.0_2						,			__lowerCAmelCase = "linear"						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "fixed_small"						,			__lowerCAmelCase = True						,			__lowerCAmelCase = "epsilon"						,			__lowerCAmelCase = jnp.floataa						,			):







            """simple docstring"""



            lowercase								=      dtype

      def 							A__			(		self						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if common is None:
                  lowercase								=      CommonSchedulerState.create(self		)

            # standard deviation of the initial noise distribution
            lowercase								=      jnp.array(1.0						,			dtype=self.dtype		)

            lowercase								=      jnp.arange(0						,			self.config.num_train_timesteps		).round()[::-1]

            return DDPMSchedulerState.create(
                common=__UpperCAmelCase						,			init_noise_sigma=__UpperCAmelCase						,			timesteps=__UpperCAmelCase						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            return sample

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = ()		):







            """simple docstring"""



            lowercase								=      self.config.num_train_timesteps // num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # rounding to avoid issues when num_inference_step is power of 3
            lowercase								=      (jnp.arange(0						,			__UpperCAmelCase		) * step_ratio).round()[::-1]

            return state.replace(
                num_inference_steps=__UpperCAmelCase						,			timesteps=__UpperCAmelCase						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None		):







            """simple docstring"""



            lowercase								=      state.common.alphas_cumprod[t]
            lowercase								=      jnp.where(t > 0						,			state.common.alphas_cumprod[t - 1]						,			jnp.array(1.0						,			dtype=self.dtype		)		)

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            lowercase								=      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

            if variance_type is None:
                  lowercase								=      self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small":
                  lowercase								=      jnp.clip(__UpperCAmelCase						,			a_min=1E-20		)
            # for rl-diffuser https://arxiv.org/abs/2205.09991
            elif variance_type == "fixed_small_log":
                  lowercase								=      jnp.log(jnp.clip(__UpperCAmelCase						,			a_min=1E-20		)		)
            elif variance_type == "fixed_large":
                  lowercase								=      state.common.betas[t]
            elif variance_type == "fixed_large_log":
                  # Glide max_log
                  lowercase								=      jnp.log(state.common.betas[t]		)
            elif variance_type == "learned":
                  return predicted_variance
            elif variance_type == "learned_range":
                  lowercase								=      variance
                  lowercase								=      state.common.betas[t]
                  lowercase								=      (predicted_variance + 1) / 2
                  lowercase								=      frac * max_log + (1 - frac) * min_log

            return variance

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      timestep

            if key is None:
                  lowercase								=      jax.random.PRNGKey(0		)

            if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                  lowercase								=      jnp.split(__UpperCAmelCase						,			sample.shape[1]						,			axis=1		)
            else:
                  lowercase								=      None

            # 1. compute alphas, betas
            lowercase								=      state.common.alphas_cumprod[t]
            lowercase								=      jnp.where(t > 0						,			state.common.alphas_cumprod[t - 1]						,			jnp.array(1.0						,			dtype=self.dtype		)		)
            lowercase								=      1 - alpha_prod_t
            lowercase								=      1 - alpha_prod_t_prev

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                  lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                  lowercase								=      model_output
            elif self.config.prediction_type == "v_prediction":
                  lowercase								=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            else:
                  raise ValueError(
                      f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
                      """ for the FlaxDDPMScheduler."""		)

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                  lowercase								=      jnp.clip(__UpperCAmelCase						,			-1						,			1		)

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowercase								=      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
            lowercase								=      state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            lowercase								=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            def random_variance():
                  lowercase								=      jax.random.split(__UpperCAmelCase						,			num=1		)
                  lowercase								=      jax.random.normal(__UpperCAmelCase						,			shape=model_output.shape						,			dtype=self.dtype		)
                  return (self._get_variance(__UpperCAmelCase						,			__UpperCAmelCase						,			predicted_variance=__UpperCAmelCase		) ** 0.5) * noise

            lowercase								=      jnp.where(t > 0						,			random_variance()						,			jnp.zeros(model_output.shape						,			dtype=self.dtype		)		)

            lowercase								=      pred_prev_sample + variance

            if not return_dict:
                  return (pred_prev_sample, state)

            return FlaxDDPMSchedulerOutput(prev_sample=__UpperCAmelCase						,			state=__UpperCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			):







            """simple docstring"""



            return add_noise_common(state.common						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			):







            """simple docstring"""



            return get_velocity_common(state.common						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase		)







      def __len__(		self		):







            """simple docstring"""



            return self.config.num_train_timesteps










"""simple docstring"""





import csv

import tweepy

# Twitter API credentials
__A											=						""""""
__A											=						""""""
__A											=						""""""
__A											=						""""""
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				tweepy.OAuthHandler(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  auth.set_access_token(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				tweepy.API(_SCREAMING_SNAKE_CASE     )

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

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

  # save most recent tweets
  alltweets.extend(_SCREAMING_SNAKE_CASE     )

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

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

    # all subsequent requests use the max_id param to prevent duplicates
    lowerCAmelCase__     :Union[str, Any]											=				api.user_timeline(
        screen_name=_SCREAMING_SNAKE_CASE	,  count=200	,  max_id=_SCREAMING_SNAKE_CASE     )

    # save most recent tweets
    alltweets.extend(_SCREAMING_SNAKE_CASE     )

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

    print(F"...{len(_SCREAMING_SNAKE_CASE     )} tweets downloaded so far"     )

  # transform the tweepy tweets into a 2D array that will populate the csv
  lowerCAmelCase__     :Optional[Any]											=				[[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:
    lowerCAmelCase__     :List[str]											=				csv.writer(_SCREAMING_SNAKE_CASE     )
    writer.writerow(['id', 'created_at', 'text']     )
    writer.writerows(_SCREAMING_SNAKE_CASE     )


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