




"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							UpperCAmelCase		(			snake_case			:					int      ,       snake_case			:					List[str]      ,       snake_case			:					Any    ):
					_lowerCAmelCase:List[Any]									=  AutoConfig.from_pretrained(lowerCAmelCase__    )
					_lowerCAmelCase:Union[str, Any]									=  FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__    )
					_lowerCAmelCase:Tuple									=  checkpoints.load_tax_checkpoint(lowerCAmelCase__    )

					_lowerCAmelCase:Dict									=  'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

					if config.model_type == "t5":
										_lowerCAmelCase:List[str]									=  'SelfAttention'
					if config.model_type == "longt5" and config.encoder_attention_type == "local":
										_lowerCAmelCase:int									=  'LocalSelfAttention'
					elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
										_lowerCAmelCase:Dict									=  '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    ):
										_lowerCAmelCase:Tuple									=  F'layers_{str(lowerCAmelCase__    )}'

										# Self-Attention
										_lowerCAmelCase:List[str]									=  tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
										_lowerCAmelCase:List[Any]									=  tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
										_lowerCAmelCase:str									=  tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
										_lowerCAmelCase:Dict									=  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":
															_lowerCAmelCase:List[str]									=  tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

										# Layer Normalization
										_lowerCAmelCase:List[Any]									=  tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

										if split_mlp_wi:
															_lowerCAmelCase:List[str]									=  tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
															_lowerCAmelCase:Dict									=  tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
										else:
															_lowerCAmelCase:Union[str, Any]									=  tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

										_lowerCAmelCase:int									=  tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

										# Layer Normalization
										_lowerCAmelCase:List[str]									=  tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

										# Assigning
										_lowerCAmelCase:Tuple									=  flax_model.params['encoder']['block'][str(lowerCAmelCase__    )]['layer']
										_lowerCAmelCase:Optional[Any]									=  tax_attention_key
										_lowerCAmelCase:List[str]									=  tax_attention_out
										_lowerCAmelCase:int									=  tax_attention_query
										_lowerCAmelCase:Optional[int]									=  tax_attention_value

										_lowerCAmelCase:Optional[Any]									=  tax_attention_layer_norm

										# Global input layer norm
										if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
															_lowerCAmelCase:Optional[Any]									=  tax_global_layer_norm

										if split_mlp_wi:
															_lowerCAmelCase:Union[str, Any]									=  tax_mlp_wi_a
															_lowerCAmelCase:Dict									=  tax_mlp_wi_a
										else:
															_lowerCAmelCase:int									=  tax_mlp_wi

										_lowerCAmelCase:Union[str, Any]									=  tax_mlp_wo
										_lowerCAmelCase:Tuple									=  tax_mlp_layer_norm

										_lowerCAmelCase:Any									=  flax_model_encoder_layer_block

					# Only for layer 0:
					_lowerCAmelCase:Tuple									=  tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
					_lowerCAmelCase:Union[str, Any]									=  tax_encoder_rel_embedding

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

					# Assigning
					_lowerCAmelCase:Dict									=  tax_model['target']['encoder']['encoder_norm']['scale']
					_lowerCAmelCase:List[Any]									=  tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_layers    ):
										_lowerCAmelCase:List[Any]									=  F'layers_{str(lowerCAmelCase__    )}'

										# Self-Attention
										_lowerCAmelCase:List[Any]									=  tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
										_lowerCAmelCase:Any									=  tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
										_lowerCAmelCase:Tuple									=  tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
										_lowerCAmelCase:Dict									=  tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

										# Layer Normalization
										_lowerCAmelCase:List[str]									=  tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
										    'scale'
										]

										# Encoder-Decoder-Attention
										_lowerCAmelCase:Optional[Any]									=  tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
										_lowerCAmelCase:Tuple									=  tax_enc_dec_attention_module['key']['kernel']
										_lowerCAmelCase:Tuple									=  tax_enc_dec_attention_module['out']['kernel']
										_lowerCAmelCase:Optional[Any]									=  tax_enc_dec_attention_module['query']['kernel']
										_lowerCAmelCase:int									=  tax_enc_dec_attention_module['value']['kernel']

										# Layer Normalization
										_lowerCAmelCase:int									=  tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

										# MLP
										if split_mlp_wi:
															_lowerCAmelCase:Tuple									=  tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
															_lowerCAmelCase:Optional[int]									=  tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
										else:
															_lowerCAmelCase:str									=  tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

										_lowerCAmelCase:str									=  tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

										# Layer Normalization
										_lowerCAmelCase:Tuple									=  tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

										# Assigning
										_lowerCAmelCase:List[Any]									=  flax_model.params['decoder']['block'][str(lowerCAmelCase__    )]['layer']
										_lowerCAmelCase:int									=  tax_attention_key
										_lowerCAmelCase:int									=  tax_attention_out
										_lowerCAmelCase:Tuple									=  tax_attention_query
										_lowerCAmelCase:List[str]									=  tax_attention_value

										_lowerCAmelCase:Tuple									=  tax_pre_attention_layer_norm

										_lowerCAmelCase:Optional[int]									=  tax_enc_dec_attention_key
										_lowerCAmelCase:List[Any]									=  tax_enc_dec_attention_out
										_lowerCAmelCase:Dict									=  tax_enc_dec_attention_query
										_lowerCAmelCase:Optional[int]									=  tax_enc_dec_attention_value

										_lowerCAmelCase:List[str]									=  tax_cross_layer_norm

										if split_mlp_wi:
															_lowerCAmelCase:List[str]									=  tax_mlp_wi_a
															_lowerCAmelCase:Union[str, Any]									=  tax_mlp_wi_a
										else:
															_lowerCAmelCase:Dict									=  tax_mlp_wi

										_lowerCAmelCase:str									=  tax_mlp_wo

										_lowerCAmelCase:Optional[int]									=  txa_mlp_layer_norm

										_lowerCAmelCase:List[Any]									=  flax_model_decoder_layer_block

					# Decoder Normalization
					_lowerCAmelCase:Dict									=  tax_model['target']['decoder']['decoder_norm']['scale']
					_lowerCAmelCase:Dict									=  txa_decoder_norm

					# Only for layer 0:
					_lowerCAmelCase:Dict									=  tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
					_lowerCAmelCase:Union[str, Any]									=  tax_decoder_rel_embedding

					# Token Embeddings
					_lowerCAmelCase:Any									=  tax_model['target']['token_embedder']['embedding']
					_lowerCAmelCase:Tuple									=  txa_token_embeddings

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

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


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












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					assert x is not None
					assert y is not None

					_lowerCAmelCase:Optional[Any]									=  len(snake_case    )
					_lowerCAmelCase:Tuple									=  len(snake_case    )

					# declaring the array for storing the dp values
					_lowerCAmelCase:Optional[Any]									=  [[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

					for i in range(1      ,       m + 1    ):
										for j in range(1      ,       n + 1    ):
															_lowerCAmelCase:Optional[int]									=  1 if x[i - 1] == y[j - 1] else 0

															_lowerCAmelCase:int									=  max(l[i - 1][j]      ,       l[i][j - 1]      ,       l[i - 1][j - 1] + match    )

					_lowerCAmelCase:List[Any]									=  ''''''
					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  m, n
					while i > 0 and j > 0:
										_lowerCAmelCase:Any									=  1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				_lowerCAmelCase:Any									=  x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
				UpperCamelCase__						=			'''AGGTAB'''
				UpperCamelCase__						=			'''GXTXAYB'''
				UpperCamelCase__						=			4
				UpperCamelCase__						=			'''GTAB'''

				UpperCamelCase__						,							UpperCamelCase__						=			longest_common_subsequence(a, b)
				print('''len =''', ln, ''', sub-sequence =''', subseq)
				import doctest

				doctest.testmod()






