






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 					lowercase_ (    __snake_case     :							Tuple	,	__snake_case     :							str	,	__snake_case     :							List[Any]      )     -> Optional[Any]:





				'''simple docstring'''


				return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							Dict	,	__snake_case     :							List[str]="attention"      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :List[str]         =   np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]      )
				snake_case__      :int         =   k_tmp.reshape(k_tmp.shape[0]	,	k_tmp.shape[1] * k_tmp.shape[2]      )
				snake_case__      :Dict         =   np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]      )
				snake_case__      :Tuple         =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,	o_tmp.shape[2]      )
				snake_case__      :Optional[Any]         =   np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]      )
				snake_case__      :Union[str, Any]         =   q_tmp.reshape(q_tmp.shape[0]	,	q_tmp.shape[1] * q_tmp.shape[2]      )
				snake_case__      :List[str]         =   np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]      )
				snake_case__      :str         =   v_tmp.reshape(v_tmp.shape[0]	,	v_tmp.shape[1] * v_tmp.shape[2]      )
				return k, o, q, v





def 					lowercase_ (    __snake_case     :							Dict	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							str	,	__snake_case     :							int=False      )     -> List[Any]:





				'''simple docstring'''


				if split_mlp_wi:
								snake_case__      :str         =   params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
								snake_case__      :str         =   params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
								snake_case__      :int         =   (wi_a, wi_a)
				else:
								snake_case__      :Optional[int]         =   params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

				snake_case__      :Optional[int]         =   params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
				return wi, wo





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Optional[Any]	,	__snake_case     :							Tuple	,	__snake_case     :							Any      )     -> Optional[int]:





				'''simple docstring'''


				return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]





def 					lowercase_ (    __snake_case     :							dict	,	*, __snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							bool = False      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Tuple         =   traverse_util.flatten_dict(variables["target"]      )
				snake_case__      :Dict         =   {"/".join(__snake_case      ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				snake_case__      :Optional[Any]         =   "encoder/encoder/mlp/wi_0/kernel" in old
				print("Split MLP:"	,	__snake_case      )

				snake_case__      :Union[str, Any]         =   collections.OrderedDict()

				# Shared embeddings.
				snake_case__      :List[str]         =   old["token_embedder/embedding"]

				# Encoder.
				for i in range(__snake_case      ):
								# Block i, layer 0 (Self Attention).
								snake_case__      :Tuple         =   tax_layer_norm_lookup(__snake_case	,	__snake_case	,	"encoder"	,	"pre_attention_layer_norm"      )
								snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :List[str]         =   tax_attention_lookup(__snake_case	,	__snake_case	,	"encoder"	,	"attention"      )
								snake_case__      :str         =   layer_norm
								snake_case__      :Optional[int]         =   k.T
								snake_case__      :Tuple         =   o.T
								snake_case__      :Optional[int]         =   q.T
								snake_case__      :List[str]         =   v.T

								# Block i, layer 1 (MLP).
								snake_case__      :Any         =   tax_layer_norm_lookup(__snake_case	,	__snake_case	,	"encoder"	,	"pre_mlp_layer_norm"      )
								snake_case__    ,						snake_case__      :Union[str, Any]         =   tax_mlp_lookup(__snake_case	,	__snake_case	,	"encoder"	,	__snake_case      )
								snake_case__      :List[Any]         =   layer_norm
								if split_mlp_wi:
												snake_case__      :int         =   wi[0].T
												snake_case__      :Optional[Any]         =   wi[1].T
								else:
												snake_case__      :List[str]         =   wi.T
								snake_case__      :Dict         =   wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												snake_case__      :str         =   tax_relpos_bias_lookup(
												    __snake_case	,	__snake_case	,	"encoder"      ).T

				snake_case__      :int         =   old["encoder/encoder_norm/scale"]

				if not scalable_attention:
								snake_case__      :List[Any]         =   tax_relpos_bias_lookup(
								    __snake_case	,	0	,	"encoder"      ).T
								snake_case__      :Union[str, Any]         =   tax_relpos_bias_lookup(
								    __snake_case	,	0	,	"decoder"      ).T

				if not is_encoder_only:
								# Decoder.
								for i in range(__snake_case      ):
												# Block i, layer 0 (Self Attention).
												snake_case__      :Dict         =   tax_layer_norm_lookup(__snake_case	,	__snake_case	,	"decoder"	,	"pre_self_attention_layer_norm"      )
												snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :List[Any]         =   tax_attention_lookup(__snake_case	,	__snake_case	,	"decoder"	,	"self_attention"      )
												snake_case__      :List[Any]         =   layer_norm
												snake_case__      :Tuple         =   k.T
												snake_case__      :Optional[int]         =   o.T
												snake_case__      :Tuple         =   q.T
												snake_case__      :List[str]         =   v.T

												# Block i, layer 1 (Cross Attention).
												snake_case__      :List[Any]         =   tax_layer_norm_lookup(__snake_case	,	__snake_case	,	"decoder"	,	"pre_cross_attention_layer_norm"      )
												snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   tax_attention_lookup(__snake_case	,	__snake_case	,	"decoder"	,	"encoder_decoder_attention"      )
												snake_case__      :List[Any]         =   layer_norm
												snake_case__      :Optional[Any]         =   k.T
												snake_case__      :List[Any]         =   o.T
												snake_case__      :str         =   q.T
												snake_case__      :Optional[Any]         =   v.T

												# Block i, layer 2 (MLP).
												snake_case__      :Optional[int]         =   tax_layer_norm_lookup(__snake_case	,	__snake_case	,	"decoder"	,	"pre_mlp_layer_norm"      )
												snake_case__    ,						snake_case__      :Dict         =   tax_mlp_lookup(__snake_case	,	__snake_case	,	"decoder"	,	__snake_case      )
												snake_case__      :int         =   layer_norm
												if split_mlp_wi:
																snake_case__      :Optional[Any]         =   wi[0].T
																snake_case__      :List[str]         =   wi[1].T
												else:
																snake_case__      :Optional[int]         =   wi.T
												snake_case__      :Optional[int]         =   wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																snake_case__      :Optional[Any]         =   tax_relpos_bias_lookup(__snake_case	,	__snake_case	,	"decoder"      ).T

								snake_case__      :Dict         =   old["decoder/decoder_norm/scale"]

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												snake_case__      :Optional[Any]         =   old["decoder/logits_dense/kernel"].T

				return new





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							bool      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								snake_case__      :Tuple         =   state_dict["shared.weight"]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												snake_case__      :Optional[Any]         =   state_dict["shared.weight"]

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print("Using shared word embeddings as lm_head."      )
												snake_case__      :Optional[int]         =   state_dict["shared.weight"]

				return state_dict





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Any	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							List[str]	,	__snake_case     :							Any      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :List[str]         =   checkpoints.load_tax_checkpoint(__snake_case      )
				snake_case__      :List[Any]         =   convert_tax_to_pytorch(
				    __snake_case	,	num_layers=config.num_layers	,	is_encoder_only=__snake_case	,	scalable_attention=__snake_case      )
				snake_case__      :Optional[Any]         =   make_state_dict(__snake_case	,	__snake_case      )
				model.load_state_dict(__snake_case	,	strict=__snake_case      )





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Tuple	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							bool = False	,	__snake_case     :							bool = False	,	)     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Dict         =   MTaConfig.from_json_file(__snake_case      )
				print(F'Building PyTorch model from configuration: {config}'      )
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								snake_case__      :Any         =   UMTaEncoderModel(__snake_case      )
				else:
								snake_case__      :List[Any]         =   UMTaForConditionalGeneration(__snake_case      )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )

				# Save pytorch-model
				print(F'Save PyTorch model to {pytorch_dump_path}'      )
				model.save_pretrained(__snake_case      )

				# Verify that we can load the checkpoint.
				model.from_pretrained(__snake_case      )
				print("Done"      )


if __name__ == "__main__":
	__UpperCAmelCase							:   Dict							=  argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
	# Required parameters
	parser.add_argument(
	    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
	)
	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
	)
	parser.add_argument(
	    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	parser.add_argument(
	    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
	)
	parser.add_argument(
	    "--scalable_attention",
	    action="store_true",
	    help="Whether the model uses scaled attention (umt5 model)",
	    default=False,
	)
	__UpperCAmelCase							:   str							=  parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)







from __future__ import annotations





def 					lowercase_ (    __snake_case     :							list      )     -> float:





				'''simple docstring'''


				if not nums:
								raise ValueError("List is empty"      )
				return sum(__snake_case      ) / len(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()