

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     TaConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
						snake_case_       =     FlaxTaForConditionalGeneration(config=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__	)

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

						# Encoder
						for layer_index in range(config.num_layers	):
												snake_case_       =     F'''layers_{str(SCREAMING_SNAKE_CASE__	)}'''

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

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

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

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

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

												# Assigning
												snake_case_       =     tax_attention_key
												snake_case_       =     tax_attention_out
												snake_case_       =     tax_attention_query
												snake_case_       =     tax_attention_value

												snake_case_       =     tax_attention_layer_norm

												if split_mlp_wi:
																		snake_case_       =     tax_mlp_wi_a
																		snake_case_       =     tax_mlp_wi_a
												else:
																		snake_case_       =     tax_mlp_wi

												snake_case_       =     tax_mlp_wo
												snake_case_       =     tax_mlp_layer_norm

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

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

						# Decoder
						for layer_index in range(config.num_decoder_layers	):
												snake_case_       =     F'''layers_{str(SCREAMING_SNAKE_CASE__	)}'''

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

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

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

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

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

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

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

												# Assigning
												snake_case_       =     tax_attention_key
												snake_case_       =     tax_attention_out
												snake_case_       =     tax_attention_query
												snake_case_       =     tax_attention_value

												snake_case_       =     tax_pre_attention_layer_norm

												snake_case_       =     tax_enc_dec_attention_key
												snake_case_       =     tax_enc_dec_attention_out
												snake_case_       =     tax_enc_dec_attention_query
												snake_case_       =     tax_enc_dec_attention_value

												snake_case_       =     tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case_       =     tax_mlp_wi_a
																		snake_case_       =     tax_mlp_wi_a
												else:
																		snake_case_       =     tax_mlp_wi

												snake_case_       =     tax_mlp_wo

												snake_case_       =     txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
					lowerCAmelCase_	     =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
					)
					parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
					parser.add_argument(
					    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
					)
					lowerCAmelCase_	     =     parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


from __future__ import annotations

from random import randrange


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if len(SCREAMING_SNAKE_CASE__	) < 2:
												return collection
						snake_case_       =     randrange(len(SCREAMING_SNAKE_CASE__	)	)  # Use random element as pivot
						snake_case_       =     collection[pivot_index]
						snake_case_       =     []  # All elements greater than pivot
						snake_case_       =     []  # All elements less than or equal to pivot

						for element in collection[:pivot_index]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						for element in collection[pivot_index + 1 :]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						return [*quick_sort(SCREAMING_SNAKE_CASE__	), pivot, *quick_sort(SCREAMING_SNAKE_CASE__	)]


if __name__ == "__main__":
					lowerCAmelCase_	     =     input('''Enter numbers separated by a comma:\n''').strip()
					lowerCAmelCase_	     =     [int(item) for item in user_input.split(''',''')]
					print(quick_sort(unsorted))