


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_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=False     ):
							if split_mlp_wi:
														__snake_case		:      str										=       params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														__snake_case		:      Optional[Any]										=       params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														__snake_case		:      str										=       (wi_a, wi_a)
							else:
														__snake_case		:      int										=       params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

							__snake_case		:      Dict										=       params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
							return wi, wo







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]







def     lowerCAmelCase_					( __lowerCamelCase							,     *, __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False     ):
							__snake_case		:      str										=       traverse_util.flatten_dict(variables["target"]     )
							__snake_case		:      Optional[int]										=       {"/".join(__lowerCamelCase     ): 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[int]										=       "encoder/encoder/mlp/wi_0/kernel" in old
							print("Split MLP:"							,     __lowerCamelCase     )

							__snake_case		:      Dict										=       collections.OrderedDict()

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

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

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

							__snake_case		:      Any										=       old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														__snake_case		:      Optional[int]										=       tax_relpos_bias_lookup(
														    __lowerCamelCase							,     0							,     "encoder"     ).T
														__snake_case		:      Any										=       tax_relpos_bias_lookup(
														    __lowerCamelCase							,     0							,     "decoder"     ).T

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

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

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

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

														__snake_case		:      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:
																					__snake_case		:      Dict										=       old["decoder/logits_dense/kernel"].T

							return new







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      int										=       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		:      Optional[Any]										=       state_dict["shared.weight"]

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

							return state_dict







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False							,     __lowerCamelCase = False							,     ):
							__snake_case		:      Optional[int]										=       MTaConfig.from_json_file(__lowerCamelCase     )
							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		:      Optional[Any]										=       UMTaEncoderModel(__lowerCamelCase     )
							else:
														__snake_case		:      List[Any]										=       UMTaForConditionalGeneration(__lowerCamelCase     )

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

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

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


if __name__ == "__main__":
			_snake_case					:		Optional[int]      =    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,
			)
			_snake_case					:		Dict      =    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,
			)





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

