


'''simple docstring'''

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= AutoConfig.from_pretrained(lowerCAmelCase__)
						__lowerCAmelCase											= FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__)
						__lowerCAmelCase											= checkpoints.load_tax_checkpoint(lowerCAmelCase__)

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

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

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

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

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

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

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

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

												__lowerCAmelCase											= tax_attention_layer_norm

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

												if split_mlp_wi:
																		__lowerCAmelCase											= tax_mlp_wi_a
																		__lowerCAmelCase											= tax_mlp_wi_a
												else:
																		__lowerCAmelCase											= tax_mlp_wi

												__lowerCAmelCase											= tax_mlp_wo
												__lowerCAmelCase											= tax_mlp_layer_norm

												__lowerCAmelCase											= flax_model_encoder_layer_block

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

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

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

						# Decoder
						for layer_index in range(config.num_layers):
												__lowerCAmelCase											= F"""layers_{str(lowerCAmelCase__)}"""

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

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

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

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

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

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

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

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

												__lowerCAmelCase											= tax_pre_attention_layer_norm

												__lowerCAmelCase											= tax_enc_dec_attention_key
												__lowerCAmelCase											= tax_enc_dec_attention_out
												__lowerCAmelCase											= tax_enc_dec_attention_query
												__lowerCAmelCase											= tax_enc_dec_attention_value

												__lowerCAmelCase											= tax_cross_layer_norm

												if split_mlp_wi:
																		__lowerCAmelCase											= tax_mlp_wi_a
																		__lowerCAmelCase											= tax_mlp_wi_a
												else:
																		__lowerCAmelCase											= tax_mlp_wi

												__lowerCAmelCase											= tax_mlp_wo

												__lowerCAmelCase											= txa_mlp_layer_norm

												__lowerCAmelCase											= flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
	_UpperCAmelCase :       List[str]										= 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 :       int										= parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






