'''simple docstring'''




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 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      List[Any]				)    ->   Optional[Any]:
							return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




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




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      str=False				)    ->   List[Any]:
							if split_mlp_wi:
														lowercase_      :    List[Any]       		= params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
														lowercase_      :    Tuple       		= params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
														lowercase_      :    str       		= (wi_a, wi_a)
							else:
														lowercase_      :    str       		= params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

							lowercase_      :    List[str]       		= params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
							return wi, wo




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      Optional[int]				)    ->   int:
							return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def 							lowerCamelCase			(   UpperCAmelCase__ :      dict   ,   *, UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      bool   ,   UpperCAmelCase__ :      bool = False				)    ->   str:
							lowercase_      :    int       		= traverse_util.flatten_dict(variables["""target"""]				)
							lowercase_      :    Dict       		= {"""/""".join(UpperCAmelCase__				): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							lowercase_      :    Tuple       		= """encoder/encoder/mlp/wi_0/kernel""" in old
							print("""Split MLP:"""   ,   UpperCAmelCase__				)

							lowercase_      :    List[Any]       		= collections.OrderedDict()

							# Shared embeddings.
							lowercase_      :    Tuple       		= old["""token_embedder/embedding"""]

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

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

							lowercase_      :    Dict       		= old["""encoder/encoder_norm/scale"""]

							if not scalable_attention:
														lowercase_      :    Dict       		= tax_relpos_bias_lookup(
														    UpperCAmelCase__   ,   0   ,   """encoder"""				).T
														lowercase_      :    List[Any]       		= tax_relpos_bias_lookup(
														    UpperCAmelCase__   ,   0   ,   """decoder"""				).T

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

																					# Block i, layer 1 (Cross Attention).
																					lowercase_      :    Tuple       		= tax_layer_norm_lookup(UpperCAmelCase__   ,   UpperCAmelCase__   ,   """decoder"""   ,   """pre_cross_attention_layer_norm"""				)
																					lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Union[str, Any]       		= tax_attention_lookup(UpperCAmelCase__   ,   UpperCAmelCase__   ,   """decoder"""   ,   """encoder_decoder_attention"""				)
																					lowercase_      :    Union[str, Any]       		= layer_norm
																					lowercase_      :    Union[str, Any]       		= k.T
																					lowercase_      :    str       		= o.T
																					lowercase_      :    Dict       		= q.T
																					lowercase_      :    Union[str, Any]       		= v.T

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

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												lowercase_      :    Tuple       		= tax_relpos_bias_lookup(UpperCAmelCase__   ,   UpperCAmelCase__   ,   """decoder"""				).T

														lowercase_      :    List[Any]       		= 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:
																					lowercase_      :    Tuple       		= old["""decoder/logits_dense/kernel"""].T

							return new




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      bool				)    ->   Dict:
							lowercase_      :    Dict       		= 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:
														lowercase_      :    Tuple       		= state_dict["""shared.weight"""]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					lowercase_      :    Union[str, 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."""				)
																					lowercase_      :    Any       		= state_dict["""shared.weight"""]

							return state_dict




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Union[str, Any]				)    ->   Dict:
							lowercase_      :    Union[str, Any]       		= checkpoints.load_tax_checkpoint(UpperCAmelCase__				)
							lowercase_      :    Tuple       		= convert_tax_to_pytorch(
							    UpperCAmelCase__   ,   num_layers=config.num_layers   ,   is_encoder_only=UpperCAmelCase__   ,   scalable_attention=UpperCAmelCase__				)
							lowercase_      :    int       		= make_state_dict(UpperCAmelCase__   ,   UpperCAmelCase__				)
							model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      bool = False   ,   UpperCAmelCase__ :      bool = False   ,   )    ->   Union[str, Any]:
							lowercase_      :    Any       		= MTaConfig.from_json_file(UpperCAmelCase__				)
							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:
														lowercase_      :    Any       		= UMTaEncoderModel(UpperCAmelCase__				)
							else:
														lowercase_      :    Any       		= UMTaForConditionalGeneration(UpperCAmelCase__				)

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

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

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


if __name__ == "__main__":
						_lowercase		:				Tuple	       =		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,
						)
						_lowercase		:				Any	       =		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,
						)






'''simple docstring'''




from __future__ import annotations




def 							lowerCamelCase			(   UpperCAmelCase__ :      list   ,   UpperCAmelCase__ :      int | None = None   ,   UpperCAmelCase__ :      int | None = None				)    ->   None:
							if start is None:
														lowercase_      :    Any       		= 0

							if end is None:
														lowercase_      :    List[Any]       		= len(UpperCAmelCase__				) - 1

							if start >= end:
														return

							lowercase_      :    Optional[int]       		= (start + end) // 2

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							slowsort(UpperCAmelCase__   ,   mid + 1   ,   UpperCAmelCase__				)

							if sequence[end] < sequence[mid]:
														lowercase_		,		lowercase_      :    Dict       		= sequence[mid], sequence[end]

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   end - 1				)


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

						testmod()





