import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							AutoConfig.from_pretrained(lowercase							)
				__lowercase 			=							FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase							)
				__lowercase 			=							checkpoints.load_tax_checkpoint(lowercase							)

				__lowercase 			=							'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

				if config.model_type == "t5":
								__lowercase 			=							'''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								__lowercase 			=							'''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__lowercase 			=							'''TransientGlobalSelfAttention'''
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''							)

				# Encoder
				for layer_index in range(config.num_layers							):
								__lowercase 			=							F"layers_{str(lowercase							)}"

								# Self-Attention
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								__lowercase 			=							tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								__lowercase 			=							flax_model.params['''encoder''']['''block'''][str(lowercase							)]['''layer''']
								__lowercase 			=							tax_attention_key
								__lowercase 			=							tax_attention_out
								__lowercase 			=							tax_attention_query
								__lowercase 			=							tax_attention_value

								__lowercase 			=							tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__lowercase 			=							tax_global_layer_norm

								if split_mlp_wi:
												__lowercase 			=							tax_mlp_wi_a
												__lowercase 			=							tax_mlp_wi_a
								else:
												__lowercase 			=							tax_mlp_wi

								__lowercase 			=							tax_mlp_wo
								__lowercase 			=							tax_mlp_layer_norm

								__lowercase 			=							flax_model_encoder_layer_block

				# Only for layer 0:
				__lowercase 			=							tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				__lowercase 			=							tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__lowercase 			=							tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								__lowercase 			=							tax_encoder_global_rel_embedding

				# Assigning
				__lowercase 			=							tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				__lowercase 			=							tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers							):
								__lowercase 			=							F"layers_{str(lowercase							)}"

								# Self-Attention
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

								# Layer Normalization
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								__lowercase 			=							tax_enc_dec_attention_module['''key''']['''kernel''']
								__lowercase 			=							tax_enc_dec_attention_module['''out''']['''kernel''']
								__lowercase 			=							tax_enc_dec_attention_module['''query''']['''kernel''']
								__lowercase 			=							tax_enc_dec_attention_module['''value''']['''kernel''']

								# Layer Normalization
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								__lowercase 			=							tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								__lowercase 			=							flax_model.params['''decoder''']['''block'''][str(lowercase							)]['''layer''']
								__lowercase 			=							tax_attention_key
								__lowercase 			=							tax_attention_out
								__lowercase 			=							tax_attention_query
								__lowercase 			=							tax_attention_value

								__lowercase 			=							tax_pre_attention_layer_norm

								__lowercase 			=							tax_enc_dec_attention_key
								__lowercase 			=							tax_enc_dec_attention_out
								__lowercase 			=							tax_enc_dec_attention_query
								__lowercase 			=							tax_enc_dec_attention_value

								__lowercase 			=							tax_cross_layer_norm

								if split_mlp_wi:
												__lowercase 			=							tax_mlp_wi_a
												__lowercase 			=							tax_mlp_wi_a
								else:
												__lowercase 			=							tax_mlp_wi

								__lowercase 			=							tax_mlp_wo

								__lowercase 			=							txa_mlp_layer_norm

								__lowercase 			=							flax_model_decoder_layer_block

				# Decoder Normalization
				__lowercase 			=							tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				__lowercase 			=							txa_decoder_norm

				# Only for layer 0:
				__lowercase 			=							tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
				__lowercase 			=							tax_decoder_rel_embedding

				# Token Embeddings
				__lowercase 			=							tax_model['''target''']['''token_embedder''']['''embedding''']
				__lowercase 			=							txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								__lowercase 			=							tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

				flax_model.save_pretrained(lowercase							)
				print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
		__a  :     str       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
		)
		parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
		parser.add_argument(
		    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
		)
		__a  :     Optional[int]       =  parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
import csv

import tweepy

# Twitter API credentials
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     List[Any]       =  """"""





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							tweepy.OAuthHandler(lowercase							,			lowercase							)
				auth.set_access_token(lowercase							,			lowercase							)
				__lowercase 			=							tweepy.API(lowercase							)

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

				# make initial request for most recent tweets (200 is the maximum allowed count)
				__lowercase 			=							api.user_timeline(screen_name=lowercase							,			count=200							)

				# save most recent tweets
				alltweets.extend(lowercase							)

				# save the id of the oldest tweet less one
				__lowercase 			=							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
								__lowercase 			=							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
								__lowercase 			=							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
				__lowercase 			=							[[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 			=							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""")