






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= AutoConfig.from_pretrained(_lowercase )
					_A								= FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase )
					_A								= checkpoints.load_tax_checkpoint(_lowercase )

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

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

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

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

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

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

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

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

										_A								= tax_attention_layer_norm

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

										if split_mlp_wi:
															_A								= tax_mlp_wi_a
															_A								= tax_mlp_wi_a
										else:
															_A								= tax_mlp_wi

										_A								= tax_mlp_wo
										_A								= tax_mlp_layer_norm

										_A								= flax_model_encoder_layer_block

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

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

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

					# Decoder
					for layer_index in range(config.num_layers ):
										_A								= f"""layers_{str(_lowercase )}"""

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

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

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

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

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

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

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

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

										_A								= tax_pre_attention_layer_norm

										_A								= tax_enc_dec_attention_key
										_A								= tax_enc_dec_attention_out
										_A								= tax_enc_dec_attention_query
										_A								= tax_enc_dec_attention_value

										_A								= tax_cross_layer_norm

										if split_mlp_wi:
															_A								= tax_mlp_wi_a
															_A								= tax_mlp_wi_a
										else:
															_A								= tax_mlp_wi

										_A								= tax_mlp_wo

										_A								= txa_mlp_layer_norm

										_A								= flax_model_decoder_layer_block

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

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

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

					# LM Head (only in v1.1 and LongT5 checkpoints)
					if "logits_dense" in tax_model["target"]["decoder"]:
										_A								= 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 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      =				parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)














def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= word.split()

					def justify(_lowercase					,			_lowercase					,			_lowercase ) -> str:
										_A								= max_width - width
										_A								= len(_lowercase )
										if len(_lowercase ) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															_A								= words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															_A								= spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															_A								= (
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(_lowercase ):
																				num_spaces_between_words_list[i] += 1
															_A								= []
															for i in range(_lowercase ):
																				# add the word
																				aligned_words_list.append(line[i] )
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
															# just add the last word to the sentence
															aligned_words_list.append(line[-1] )
															# join the aligned words list to form a justified line
															return "".join(_lowercase )

					_A								= []
					_A								= []
					_A								= 0
					for word in words:
										if width + len(_lowercase ) + len(_lowercase ) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(_lowercase )
															width += len(_lowercase )
										else:
															# justify the line and add it to result
															answer.append(justify(_lowercase					,			_lowercase					,			_lowercase ) )
															# reset new line and new width
															_A							,_A								= [word], len(_lowercase )
					_A								= max_width - width - len(_lowercase )
					answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
					return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()



