

import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    def get_masked_lm_array(_lowerCamelCase   ):
        _lowerCAmelCase       :   str  =    f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        _lowerCAmelCase       :   Union[str, Any]  =    tf.train.load_variable(lowerCamelCase__ ,   lowerCamelCase__   )

        if "kernel" in name:
            _lowerCAmelCase       :   Any  =    array.transpose()

        return torch.from_numpy(lowerCamelCase__   )

    def get_encoder_array(_lowerCamelCase   ):
        _lowerCAmelCase       :   Any  =    f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        _lowerCAmelCase       :   Tuple  =    tf.train.load_variable(lowerCamelCase__ ,   lowerCamelCase__   )

        if "kernel" in name:
            _lowerCAmelCase       :   str  =    array.transpose()

        return torch.from_numpy(lowerCamelCase__   )

    def get_encoder_layer_array(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   List[Any]  =    f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        _lowerCAmelCase       :   Union[str, Any]  =    tf.train.load_variable(lowerCamelCase__ ,   lowerCamelCase__   )

        if "kernel" in name:
            _lowerCAmelCase       :   str  =    array.transpose()

        return torch.from_numpy(lowerCamelCase__   )

    def get_encoder_attention_layer_array(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   str  =    f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        _lowerCAmelCase       :   Optional[Any]  =    tf.train.load_variable(lowerCamelCase__ ,   lowerCamelCase__   )
        _lowerCAmelCase       :   Tuple  =    array.reshape(lowerCamelCase__   )

        if "kernel" in name:
            _lowerCAmelCase       :   int  =    array.transpose()

        return torch.from_numpy(lowerCamelCase__   )

    print(f"""Loading model based on config from {config_path}..."""   )
    _lowerCAmelCase       :   Union[str, Any]  =    BertConfig.from_json_file(lowerCamelCase__   )
    _lowerCAmelCase       :   Optional[Any]  =    BertForMaskedLM(lowerCamelCase__   )

    # Layers
    for layer_index in range(0 ,   config.num_hidden_layers   ):
        _lowerCAmelCase       :   BertLayer  =    model.bert.encoder.layer[layer_index]

        # Self-attention
        _lowerCAmelCase       :   BertSelfAttention  =    layer.attention.self

        _lowerCAmelCase       :   Union[str, Any]  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_query_dense/kernel' ,   self_attn.query.weight.data.shape   )
        _lowerCAmelCase       :   Dict  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_query_dense/bias' ,   self_attn.query.bias.data.shape   )
        _lowerCAmelCase       :   Tuple  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_key_dense/kernel' ,   self_attn.key.weight.data.shape   )
        _lowerCAmelCase       :   Union[str, Any]  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_key_dense/bias' ,   self_attn.key.bias.data.shape   )
        _lowerCAmelCase       :   List[str]  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_value_dense/kernel' ,   self_attn.value.weight.data.shape   )
        _lowerCAmelCase       :   Union[str, Any]  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_value_dense/bias' ,   self_attn.value.bias.data.shape   )

        # Self-attention Output
        _lowerCAmelCase       :   BertSelfOutput  =    layer.attention.output

        _lowerCAmelCase       :   Any  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_output_dense/kernel' ,   self_output.dense.weight.data.shape   )
        _lowerCAmelCase       :   str  =    get_encoder_attention_layer_array(
            lowerCamelCase__ ,   '_output_dense/bias' ,   self_output.dense.bias.data.shape   )

        _lowerCAmelCase       :   List[str]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_attention_layer_norm/gamma'   )
        _lowerCAmelCase       :   Tuple  =    get_encoder_layer_array(lowerCamelCase__ ,   '_attention_layer_norm/beta'   )

        # Intermediate
        _lowerCAmelCase       :   BertIntermediate  =    layer.intermediate

        _lowerCAmelCase       :   Union[str, Any]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_intermediate_dense/kernel'   )
        _lowerCAmelCase       :   Any  =    get_encoder_layer_array(lowerCamelCase__ ,   '_intermediate_dense/bias'   )

        # Output
        _lowerCAmelCase       :   BertOutput  =    layer.output

        _lowerCAmelCase       :   Union[str, Any]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_output_dense/kernel'   )
        _lowerCAmelCase       :   Optional[int]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_output_dense/bias'   )

        _lowerCAmelCase       :   List[str]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_output_layer_norm/gamma'   )
        _lowerCAmelCase       :   List[str]  =    get_encoder_layer_array(lowerCamelCase__ ,   '_output_layer_norm/beta'   )

    # Embeddings
    _lowerCAmelCase       :   int  =    get_encoder_array('_position_embedding_layer/embeddings'   )
    _lowerCAmelCase       :   Optional[Any]  =    get_encoder_array('_type_embedding_layer/embeddings'   )
    _lowerCAmelCase       :   Any  =    get_encoder_array('_embedding_norm_layer/gamma'   )
    _lowerCAmelCase       :   List[str]  =    get_encoder_array('_embedding_norm_layer/beta'   )

    # LM Head
    _lowerCAmelCase       :   List[Any]  =    model.cls.predictions.transform

    _lowerCAmelCase       :   List[Any]  =    get_masked_lm_array('dense/kernel'   )
    _lowerCAmelCase       :   Optional[Any]  =    get_masked_lm_array('dense/bias'   )

    _lowerCAmelCase       :   Optional[int]  =    get_masked_lm_array('layer_norm/gamma'   )
    _lowerCAmelCase       :   int  =    get_masked_lm_array('layer_norm/beta'   )

    _lowerCAmelCase       :   List[str]  =    get_masked_lm_array('embedding_table'   )

    # Pooling
    _lowerCAmelCase       :   Union[str, Any]  =    BertPooler(config=lowerCamelCase__   )
    _lowerCAmelCase       :   BertPooler  =    get_encoder_array('_pooler_layer/kernel'   )
    _lowerCAmelCase       :   BertPooler  =    get_encoder_array('_pooler_layer/bias'   )

    # Export final model
    model.save_pretrained(lowerCamelCase__   )

    # Integration test - should load without any errors ;)
    _lowerCAmelCase       :   Optional[int]  =    BertForMaskedLM.from_pretrained(lowerCamelCase__   )
    print(new_model.eval()   )

    print('Model conversion was done sucessfully!'   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 parser.add_argument(
     """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
 )
 parser.add_argument(
     """--bert_config_file""",
     type=str,
     required=True,
     help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""",
     type=str,
     required=True,
     help="""Path to the output PyTorch model.""",
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





