



"""simple docstring"""


import csv

import tweepy

# Twitter API credentials
snake_case_    					=	""""""
snake_case_    					=	""""""
snake_case_    					=	""""""
snake_case_    					=	""""""

def 			_lowerCAmelCase				(       lowercase_						):
      # authorize twitter, initialize tweepy
      UpperCAmelCase					=			tweepy.OAuthHandler(lowercase_		,     lowercase_						)
      auth.set_access_token(lowercase_		,     lowercase_						)
      UpperCAmelCase					=			tweepy.API(lowercase_						)

      # 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=lowercase_		,     count=200						)

      # save most recent tweets
      alltweets.extend(lowercase_						)

      # 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(lowercase_						) > 0:
            print(F"""getting tweets before {oldest}"""						)

            # all subsequent requests use the max_id param to prevent duplicates
            UpperCAmelCase					=			api.user_timeline(
                screen_name=lowercase_		,     count=200		,     max_id=lowercase_						)

            # save most recent tweets
            alltweets.extend(lowercase_						)

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

            print(F"""...{len(lowercase_						)} 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(lowercase_						)
            writer.writerow(['id', 'created_at', 'text']						)
            writer.writerows(lowercase_						)


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










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





