






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     _A  (							SCREAMING_SNAKE_CASE__    :      str	,    SCREAMING_SNAKE_CASE__    :      Optional[Any]	,    SCREAMING_SNAKE_CASE__    :      Optional[Any]			):
    def get_masked_lm_array(SCREAMING_SNAKE_CASE__    :      Optional[Any]			):
        UpperCamelCase			:Union[str, Any]      				=  F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        UpperCamelCase			:Tuple      				=  tf.train.load_variable(_UpperCAmelCase	,    _UpperCAmelCase			)

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

        return torch.from_numpy(_UpperCAmelCase			)

    def get_encoder_array(SCREAMING_SNAKE_CASE__    :      int			):
        UpperCamelCase			:List[Any]      				=  F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        UpperCamelCase			:Union[str, Any]      				=  tf.train.load_variable(_UpperCAmelCase	,    _UpperCAmelCase			)

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

        return torch.from_numpy(_UpperCAmelCase			)

    def get_encoder_layer_array(SCREAMING_SNAKE_CASE__    :      Optional[Any]	,    SCREAMING_SNAKE_CASE__    :      Tuple			):
        UpperCamelCase			:Dict      				=  F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        UpperCamelCase			:Dict      				=  tf.train.load_variable(_UpperCAmelCase	,    _UpperCAmelCase			)

        if "kernel" in name:
            UpperCamelCase			:Optional[Any]      				=  array.transpose()

        return torch.from_numpy(_UpperCAmelCase			)

    def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE__    :      List[Any]	,    SCREAMING_SNAKE_CASE__    :      Tuple	,    SCREAMING_SNAKE_CASE__    :      List[str]			):
        UpperCamelCase			:Optional[Any]      				=  F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        UpperCamelCase			:List[Any]      				=  tf.train.load_variable(_UpperCAmelCase	,    _UpperCAmelCase			)
        UpperCamelCase			:Optional[Any]      				=  array.reshape(_UpperCAmelCase			)

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

        return torch.from_numpy(_UpperCAmelCase			)

    print(F'''Loading model based on config from {config_path}...'''			)
    UpperCamelCase			:List[Any]      				=  BertConfig.from_json_file(_UpperCAmelCase			)
    UpperCamelCase			:Union[str, Any]      				=  BertForMaskedLM(_UpperCAmelCase			)

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

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

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

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

        UpperCamelCase			:Any      				=  get_encoder_attention_layer_array(
            _UpperCAmelCase	,    '''_output_dense/kernel'''	,    self_output.dense.weight.data.shape			)
        UpperCamelCase			:int      				=  get_encoder_attention_layer_array(
            _UpperCAmelCase	,    '''_output_dense/bias'''	,    self_output.dense.bias.data.shape			)

        UpperCamelCase			:Optional[Any]      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_attention_layer_norm/gamma'''			)
        UpperCamelCase			:Union[str, Any]      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_attention_layer_norm/beta'''			)

        # Intermediate
        UpperCamelCase			:BertIntermediate      				=  layer.intermediate

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

        # Output
        UpperCamelCase			:BertOutput      				=  layer.output

        UpperCamelCase			:List[Any]      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_output_dense/kernel'''			)
        UpperCamelCase			:str      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_output_dense/bias'''			)

        UpperCamelCase			:Optional[Any]      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_output_layer_norm/gamma'''			)
        UpperCamelCase			:Dict      				=  get_encoder_layer_array(_UpperCAmelCase	,    '''_output_layer_norm/beta'''			)

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

    # LM Head
    UpperCamelCase			:Dict      				=  model.cls.predictions.transform

    UpperCamelCase			:Optional[Any]      				=  get_masked_lm_array('''dense/kernel'''			)
    UpperCamelCase			:int      				=  get_masked_lm_array('''dense/bias'''			)

    UpperCamelCase			:Any      				=  get_masked_lm_array('''layer_norm/gamma'''			)
    UpperCamelCase			:Optional[Any]      				=  get_masked_lm_array('''layer_norm/beta'''			)

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

    # Pooling
    UpperCamelCase			:Any      				=  BertPooler(config=_UpperCAmelCase			)
    UpperCamelCase			:BertPooler      				=  get_encoder_array('''_pooler_layer/kernel'''			)
    UpperCamelCase			:BertPooler      				=  get_encoder_array('''_pooler_layer/bias'''			)

    # Export final model
    model.save_pretrained(_UpperCAmelCase			)

    # Integration test - should load without any errors ;)
    UpperCamelCase			:Dict      				=  BertForMaskedLM.from_pretrained(_UpperCAmelCase			)
    print(new_model.eval()			)

    print('''Model conversion was done sucessfully!'''			)


if __name__ == "__main__":
 __snake_case				   =    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.""",
 )
 __snake_case				   =    parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












from __future__ import annotations

_UpperCAmelCase							:			Tuple        =							"""#"""

class   lowerCAmelCase				:
 def __init__( self    : Optional[Any]      )     ->      None:
     lowerCamelCase__       : dict   					=						{}
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      None:
     lowerCamelCase__       : int   					=						self._trie
     for char in text:
         if char not in trie:
             lowerCamelCase__       : Tuple   					=						{}
         lowerCamelCase__       : Dict   					=						trie[char]
     lowerCamelCase__       : Optional[int]   					=						True
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      tuple | list:
     lowerCamelCase__       : Tuple   					=						self._trie
     for char in prefix:
         if char in trie:
             lowerCamelCase__       : List[Any]   					=						trie[char]
         else:
             return []
     return self._elements(UpperCAmelCase      )







 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : dict      )     ->      tuple:
     lowerCamelCase__       : Optional[Any]   					=						[]
     for c, v in d.items():
         lowerCamelCase__       : Optional[Any]   					=						[' '] if c == END else [(c + s) for s in self._elements(UpperCAmelCase      )]
         result.extend(UpperCAmelCase      )
     return tuple(UpperCAmelCase      )


_UpperCAmelCase							:			str        =							Trie()
_UpperCAmelCase							:			Tuple        =							("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
    trie.insert_word(word)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	tuple:
    lowerCamelCase__       : str   					=						trie.find_word(_UpperCAmelCase					)
    return tuple(string + word for word in suffixes					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    print(autocomplete_using_trie('de'					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





