import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   AutoConfig.from_pretrained(__lowercase						)
						_snake_case						:					int    =   FlaxAutoModelForSeqaSeqLM.from_config(config=__lowercase						)
						_snake_case						:					Any    =   checkpoints.load_tax_checkpoint(__lowercase						)

						_snake_case						:					int    =   "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

						if config.model_type == "t5":
												_snake_case						:					Optional[int]    =   "SelfAttention"
						if config.model_type == "longt5" and config.encoder_attention_type == "local":
												_snake_case						:					Optional[Any]    =   "LocalSelfAttention"
						elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												_snake_case						:					Tuple    =   "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						):
												_snake_case						:					Optional[Any]    =   F"""layers_{str(__lowercase						)}"""

												# Self-Attention
												_snake_case						:					List[Any]    =   tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
												_snake_case						:					Optional[int]    =   tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
												_snake_case						:					List[str]    =   tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
												_snake_case						:					Optional[int]    =   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":
																		_snake_case						:					Union[str, Any]    =   tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

												# Layer Normalization
												_snake_case						:					Tuple    =   tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

												if split_mlp_wi:
																		_snake_case						:					Optional[Any]    =   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		_snake_case						:					Any    =   tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
												else:
																		_snake_case						:					Optional[int]    =   tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

												_snake_case						:					List[str]    =   tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												_snake_case						:					str    =   tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_snake_case						:					Optional[int]    =   flax_model.params["encoder"]["block"][str(__lowercase						)]["layer"]
												_snake_case						:					int    =   tax_attention_key
												_snake_case						:					Dict    =   tax_attention_out
												_snake_case						:					str    =   tax_attention_query
												_snake_case						:					Tuple    =   tax_attention_value

												_snake_case						:					List[Any]    =   tax_attention_layer_norm

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

												if split_mlp_wi:
																		_snake_case						:					Any    =   tax_mlp_wi_a
																		_snake_case						:					Optional[Any]    =   tax_mlp_wi_a
												else:
																		_snake_case						:					Dict    =   tax_mlp_wi

												_snake_case						:					List[str]    =   tax_mlp_wo
												_snake_case						:					int    =   tax_mlp_layer_norm

												_snake_case						:					List[Any]    =   flax_model_encoder_layer_block

						# Only for layer 0:
						_snake_case						:					Optional[int]    =   tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
						_snake_case						:					Dict    =   tax_encoder_rel_embedding

						# Side/global relative position_bias + layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												_snake_case						:					Union[str, Any]    =   tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
												_snake_case						:					Optional[Any]    =   tax_encoder_global_rel_embedding

						# Assigning
						_snake_case						:					Union[str, Any]    =   tax_model["target"]["encoder"]["encoder_norm"]["scale"]
						_snake_case						:					Optional[int]    =   tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_layers						):
												_snake_case						:					Tuple    =   F"""layers_{str(__lowercase						)}"""

												# Self-Attention
												_snake_case						:					Any    =   tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
												_snake_case						:					Any    =   tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
												_snake_case						:					str    =   tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
												_snake_case						:					Optional[Any]    =   tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

												# Layer Normalization
												_snake_case						:					Optional[Any]    =   tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
												    "scale"
												]

												# Encoder-Decoder-Attention
												_snake_case						:					List[str]    =   tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
												_snake_case						:					Any    =   tax_enc_dec_attention_module["key"]["kernel"]
												_snake_case						:					List[str]    =   tax_enc_dec_attention_module["out"]["kernel"]
												_snake_case						:					Tuple    =   tax_enc_dec_attention_module["query"]["kernel"]
												_snake_case						:					Dict    =   tax_enc_dec_attention_module["value"]["kernel"]

												# Layer Normalization
												_snake_case						:					Union[str, Any]    =   tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

												# MLP
												if split_mlp_wi:
																		_snake_case						:					int    =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		_snake_case						:					Optional[Any]    =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
												else:
																		_snake_case						:					Any    =   tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

												_snake_case						:					Dict    =   tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												_snake_case						:					str    =   tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												_snake_case						:					List[Any]    =   flax_model.params["decoder"]["block"][str(__lowercase						)]["layer"]
												_snake_case						:					int    =   tax_attention_key
												_snake_case						:					str    =   tax_attention_out
												_snake_case						:					List[str]    =   tax_attention_query
												_snake_case						:					Optional[int]    =   tax_attention_value

												_snake_case						:					Dict    =   tax_pre_attention_layer_norm

												_snake_case						:					List[Any]    =   tax_enc_dec_attention_key
												_snake_case						:					Any    =   tax_enc_dec_attention_out
												_snake_case						:					Any    =   tax_enc_dec_attention_query
												_snake_case						:					Union[str, Any]    =   tax_enc_dec_attention_value

												_snake_case						:					Union[str, Any]    =   tax_cross_layer_norm

												if split_mlp_wi:
																		_snake_case						:					List[str]    =   tax_mlp_wi_a
																		_snake_case						:					int    =   tax_mlp_wi_a
												else:
																		_snake_case						:					int    =   tax_mlp_wi

												_snake_case						:					Tuple    =   tax_mlp_wo

												_snake_case						:					Optional[int]    =   txa_mlp_layer_norm

												_snake_case						:					Union[str, Any]    =   flax_model_decoder_layer_block

						# Decoder Normalization
						_snake_case						:					List[str]    =   tax_model["target"]["decoder"]["decoder_norm"]["scale"]
						_snake_case						:					Union[str, Any]    =   txa_decoder_norm

						# Only for layer 0:
						_snake_case						:					Dict    =   tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
						_snake_case						:					Dict    =   tax_decoder_rel_embedding

						# Token Embeddings
						_snake_case						:					str    =   tax_model["target"]["token_embedder"]["embedding"]
						_snake_case						:					Any    =   txa_token_embeddings

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

						flax_model.save_pretrained(__lowercase						)
						print("T5X Model was sucessfully converted!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   Optional[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.'
				)
				__SCREAMING_SNAKE_CASE						:   int         =			parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()