import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   TaConfig.from_pretrained(__lowerCamelCase						)
					__a							=   FlaxTaForConditionalGeneration(config=__lowerCamelCase						)
					__a							=   checkpoints.load_tax_checkpoint(__lowerCamelCase						)

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

					# Encoder
					for layer_index in range(config.num_layers						):
										__a							=   f'''layers_{str(__lowerCamelCase						)}'''

										# 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']

										# 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							=   tax_attention_key
										__a							=   tax_attention_out
										__a							=   tax_attention_query
										__a							=   tax_attention_value

										__a							=   tax_attention_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

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

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

					# Decoder
					for layer_index in range(config.num_decoder_layers						):
										__a							=   f'''layers_{str(__lowerCamelCase						)}'''

										# 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']['key'][
										    'kernel'
										]
										__a							=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
										    'kernel'
										]
										__a							=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
										    'kernel'
										]
										__a							=   tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['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							=   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

					# 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 checkpoints)
					if "logits_dense" in tax_model["target"]["decoder"]:
										__a							=   tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
	lowerCamelCase_				:		Dict       						=       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."""
	)
	lowerCamelCase_				:		str       						=       parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




