"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							AutoConfig.from_pretrained(lowerCAmelCase_       )
				UpperCAmelCase__             =							FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase_       )
				UpperCAmelCase__             =							checkpoints.load_tax_checkpoint(lowerCAmelCase_       )

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

				if config.model_type == "t5":
								UpperCAmelCase__             =							'SelfAttention'
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								UpperCAmelCase__             =							'LocalSelfAttention'
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								UpperCAmelCase__             =							'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       ):
								UpperCAmelCase__             =							f'''layers_{str(lowerCAmelCase_       )}'''

								# Self-Attention
								UpperCAmelCase__             =							tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
								UpperCAmelCase__             =							tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
								UpperCAmelCase__             =							tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
								UpperCAmelCase__             =							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":
												UpperCAmelCase__             =							tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

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

								UpperCAmelCase__             =							tax_attention_layer_norm

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

								if split_mlp_wi:
												UpperCAmelCase__             =							tax_mlp_wi_a
												UpperCAmelCase__             =							tax_mlp_wi_a
								else:
												UpperCAmelCase__             =							tax_mlp_wi

								UpperCAmelCase__             =							tax_mlp_wo
								UpperCAmelCase__             =							tax_mlp_layer_norm

								UpperCAmelCase__             =							flax_model_encoder_layer_block

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

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

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

				# Decoder
				for layer_index in range(config.num_layers       ):
								UpperCAmelCase__             =							f'''layers_{str(lowerCAmelCase_       )}'''

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

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

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

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

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

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

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

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

								UpperCAmelCase__             =							tax_pre_attention_layer_norm

								UpperCAmelCase__             =							tax_enc_dec_attention_key
								UpperCAmelCase__             =							tax_enc_dec_attention_out
								UpperCAmelCase__             =							tax_enc_dec_attention_query
								UpperCAmelCase__             =							tax_enc_dec_attention_value

								UpperCAmelCase__             =							tax_cross_layer_norm

								if split_mlp_wi:
												UpperCAmelCase__             =							tax_mlp_wi_a
												UpperCAmelCase__             =							tax_mlp_wi_a
								else:
												UpperCAmelCase__             =							tax_mlp_wi

								UpperCAmelCase__             =							tax_mlp_wo

								UpperCAmelCase__             =							txa_mlp_layer_norm

								UpperCAmelCase__             =							flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
						lowerCAmelCase__  :  Any						     =	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.'
						)
						lowerCAmelCase__  :  Tuple						     =	parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   if depth < 0:
      raise ValueError('Depth cannot be less than 0'	)

   if len(lowerCAmelCase_	) == 0:
      raise ValueError('Scores cannot be empty'	)

   if depth == height:
      return scores[node_index]

   if is_max:
      return max(
          minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

   return min(
       minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

