
'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    TaConfig.from_pretrained(lowercase__		)
						lowercase__								=    FlaxTaForConditionalGeneration(config=lowercase__		)
						lowercase__								=    checkpoints.load_tax_checkpoint(lowercase__		)

						lowercase__								=    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

						# 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"""]

												# 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__								=    tax_attention_key
												lowercase__								=    tax_attention_out
												lowercase__								=    tax_attention_query
												lowercase__								=    tax_attention_value

												lowercase__								=    tax_attention_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

						# Only for layer 0:
						lowercase__								=    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
						lowercase__								=    tax_encoder_rel_embedding

						# Assigning
						lowercase__								=    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
						lowercase__								=    tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_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"""]["""key"""][
												    """kernel"""
												]
												lowercase__								=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
												    """kernel"""
												]
												lowercase__								=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
												    """kernel"""
												]
												lowercase__								=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""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__								=    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

						# 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 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       =      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."
				)
				__A       =      parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






