


import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  True
  A__=  False
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					Tuple ):




    """simple docstring"""







    UpperCAmelCase__										=     []
    UpperCAmelCase__										=     []

    for i in range(self.num_layers ):
      UpperCAmelCase__										=     self.in_channels if i == 0 else self.out_channels

      UpperCAmelCase__										=     FlaxResnetBlockaD(
          in_channels=lowerCAmelCase_		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
      resnets.append(lowerCAmelCase_ )

      UpperCAmelCase__										=     FlaxTransformeraDModel(
          in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
      attentions.append(lowerCAmelCase_ )

    UpperCAmelCase__										=     resnets
    UpperCAmelCase__										=     attentions

    if self.add_downsample:
      UpperCAmelCase__										=     FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					int		,						_lowercase      :					Tuple		,						_lowercase      :					Union[str, Any]=True ):




    """simple docstring"""







    UpperCAmelCase__										=     ()

    for resnet, attn in zip(self.resnets		,						self.attentions ):
      UpperCAmelCase__										=     resnet(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )
      UpperCAmelCase__										=     attn(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )
      output_states += (hidden_states,)

    if self.add_downsample:
      UpperCAmelCase__										=     self.downsamplers_a(lowerCAmelCase_ )
      output_states += (hidden_states,)

    return hidden_states, output_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  True
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					Dict ):




    """simple docstring"""







    UpperCAmelCase__										=     []

    for i in range(self.num_layers ):
      UpperCAmelCase__										=     self.in_channels if i == 0 else self.out_channels

      UpperCAmelCase__										=     FlaxResnetBlockaD(
          in_channels=lowerCAmelCase_		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
      resnets.append(lowerCAmelCase_ )
    UpperCAmelCase__										=     resnets

    if self.add_downsample:
      UpperCAmelCase__										=     FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Dict		,						_lowercase      :					Dict		,						_lowercase      :					Any		,						_lowercase      :					List[str]=True ):




    """simple docstring"""







    UpperCAmelCase__										=     ()

    for resnet in self.resnets:
      UpperCAmelCase__										=     resnet(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )
      output_states += (hidden_states,)

    if self.add_downsample:
      UpperCAmelCase__										=     self.downsamplers_a(lowerCAmelCase_ )
      output_states += (hidden_states,)

    return hidden_states, output_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  True
  A__=  False
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					int ):




    """simple docstring"""







    UpperCAmelCase__										=     []
    UpperCAmelCase__										=     []

    for i in range(self.num_layers ):
      UpperCAmelCase__										=     self.in_channels if (i == self.num_layers - 1) else self.out_channels
      UpperCAmelCase__										=     self.prev_output_channel if i == 0 else self.out_channels

      UpperCAmelCase__										=     FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
      resnets.append(lowerCAmelCase_ )

      UpperCAmelCase__										=     FlaxTransformeraDModel(
          in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
      attentions.append(lowerCAmelCase_ )

    UpperCAmelCase__										=     resnets
    UpperCAmelCase__										=     attentions

    if self.add_upsample:
      UpperCAmelCase__										=     FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Dict		,						_lowercase      :					Optional[int]		,						_lowercase      :					Any		,						_lowercase      :					int		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[Any]=True ):




    """simple docstring"""







    for resnet, attn in zip(self.resnets		,						self.attentions ):
      # pop res hidden states
      UpperCAmelCase__										=     res_hidden_states_tuple[-1]
      UpperCAmelCase__										=     res_hidden_states_tuple[:-1]
      UpperCAmelCase__										=     jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1 )

      UpperCAmelCase__										=     resnet(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )
      UpperCAmelCase__										=     attn(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )

    if self.add_upsample:
      UpperCAmelCase__										=     self.upsamplers_a(lowerCAmelCase_ )

    return hidden_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  True
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




    """simple docstring"""







    UpperCAmelCase__										=     []

    for i in range(self.num_layers ):
      UpperCAmelCase__										=     self.in_channels if (i == self.num_layers - 1) else self.out_channels
      UpperCAmelCase__										=     self.prev_output_channel if i == 0 else self.out_channels

      UpperCAmelCase__										=     FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
      resnets.append(lowerCAmelCase_ )

    UpperCAmelCase__										=     resnets

    if self.add_upsample:
      UpperCAmelCase__										=     FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Optional[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					str		,						_lowercase      :					Optional[Any]=True ):




    """simple docstring"""







    for resnet in self.resnets:
      # pop res hidden states
      UpperCAmelCase__										=     res_hidden_states_tuple[-1]
      UpperCAmelCase__										=     res_hidden_states_tuple[:-1]
      UpperCAmelCase__										=     jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1 )

      UpperCAmelCase__										=     resnet(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )

    if self.add_upsample:
      UpperCAmelCase__										=     self.upsamplers_a(lowerCAmelCase_ )

    return hidden_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




    """simple docstring"""







    UpperCAmelCase__										=     [
        FlaxResnetBlockaD(
            in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
    ]

    UpperCAmelCase__										=     []

    for _ in range(self.num_layers ):
      UpperCAmelCase__										=     FlaxTransformeraDModel(
          in_channels=self.in_channels		,						n_heads=self.num_attention_heads		,						d_head=self.in_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
      attentions.append(lowerCAmelCase_ )

      UpperCAmelCase__										=     FlaxResnetBlockaD(
          in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
      resnets.append(lowerCAmelCase_ )

    UpperCAmelCase__										=     resnets
    UpperCAmelCase__										=     attentions






  def __call__(					self      :					Dict		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any]		,						_lowercase      :					Dict		,						_lowercase      :					Tuple=True ):




    """simple docstring"""







    UpperCAmelCase__										=     self.resnets[0](lowerCAmelCase_		,						lowerCAmelCase_ )
    for attn, resnet in zip(self.attentions		,						self.resnets[1:] ):
      UpperCAmelCase__										=     attn(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )
      UpperCAmelCase__										=     resnet(lowerCAmelCase_		,						lowerCAmelCase_		,						deterministic=lowerCAmelCase_ )

    return hidden_states









import csv

import tweepy

# Twitter API credentials
A      = ""
A      = ""
A      = ""
A      = ""





def __UpperCAmelCase       (		__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     tweepy.OAuthHandler(__A ,						__A			)
       auth.set_access_token(__A ,						__A			)
       UpperCAmelCase__										=     tweepy.API(__A			)

       # initialize a list to hold all the tweepy Tweets
       UpperCAmelCase__										=     []

       # make initial request for most recent tweets (200 is the maximum allowed count)
       UpperCAmelCase__										=     api.user_timeline(screen_name=__A ,						count=2_0_0			)

       # save most recent tweets
       alltweets.extend(__A			)

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

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

              # all subsequent requests use the max_id param to prevent duplicates
              UpperCAmelCase__										=     api.user_timeline(
                  screen_name=__A ,						count=2_0_0 ,						max_id=__A			)

              # save most recent tweets
              alltweets.extend(__A			)

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

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

       # transform the tweepy tweets into a 2D array that will populate the csv
       UpperCAmelCase__										=     [[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:
              UpperCAmelCase__										=     csv.writer(__A			)
              writer.writerow(["id", "created_at", "text"]			)
              writer.writerows(__A			)


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





