


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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Dict:
    def get_masked_lm_array(lowerCamelCase_ ):
        _lowercase       :							str      		=		F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        _lowercase       :							Dict      		=		tf.train.load_variable(lowerCamelCase_							, lowerCamelCase_ )

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

        return torch.from_numpy(lowerCamelCase_ )

    def get_encoder_array(lowerCamelCase_ ):
        _lowercase       :							Dict      		=		F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        _lowercase       :							Union[str, Any]      		=		tf.train.load_variable(lowerCamelCase_							, lowerCamelCase_ )

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

        return torch.from_numpy(lowerCamelCase_ )

    def get_encoder_layer_array(lowerCamelCase_							, lowerCamelCase_ ):
        _lowercase       :							Optional[Any]      		=		F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        _lowercase       :							Optional[int]      		=		tf.train.load_variable(lowerCamelCase_							, lowerCamelCase_ )

        if "kernel" in name:
            _lowercase       :							Union[str, Any]      		=		array.transpose()

        return torch.from_numpy(lowerCamelCase_ )

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

        if "kernel" in name:
            _lowercase       :							Tuple      		=		array.transpose()

        return torch.from_numpy(lowerCamelCase_ )

    print(F'''Loading model based on config from {config_path}...''' )
    _lowercase       :							List[Any]      		=		BertConfig.from_json_file(lowerCamelCase_ )
    _lowercase       :							Optional[Any]      		=		BertForMaskedLM(lowerCamelCase_ )

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

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

        _lowercase       :							Optional[Any]      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_query_dense/kernel'							, self_attn.query.weight.data.shape )
        _lowercase       :							Any      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_query_dense/bias'							, self_attn.query.bias.data.shape )
        _lowercase       :							Tuple      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_key_dense/kernel'							, self_attn.key.weight.data.shape )
        _lowercase       :							Tuple      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_key_dense/bias'							, self_attn.key.bias.data.shape )
        _lowercase       :							str      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_value_dense/kernel'							, self_attn.value.weight.data.shape )
        _lowercase       :							Optional[int]      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_value_dense/bias'							, self_attn.value.bias.data.shape )

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

        _lowercase       :							Tuple      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_output_dense/kernel'							, self_output.dense.weight.data.shape )
        _lowercase       :							List[Any]      		=		get_encoder_attention_layer_array(
            lowerCamelCase_							, '_output_dense/bias'							, self_output.dense.bias.data.shape )

        _lowercase       :							Optional[int]      		=		get_encoder_layer_array(lowerCamelCase_							, '_attention_layer_norm/gamma' )
        _lowercase       :							Union[str, Any]      		=		get_encoder_layer_array(lowerCamelCase_							, '_attention_layer_norm/beta' )

        # Intermediate
        _lowercase       :							BertIntermediate      		=		layer.intermediate

        _lowercase       :							List[str]      		=		get_encoder_layer_array(lowerCamelCase_							, '_intermediate_dense/kernel' )
        _lowercase       :							List[str]      		=		get_encoder_layer_array(lowerCamelCase_							, '_intermediate_dense/bias' )

        # Output
        _lowercase       :							BertOutput      		=		layer.output

        _lowercase       :							List[Any]      		=		get_encoder_layer_array(lowerCamelCase_							, '_output_dense/kernel' )
        _lowercase       :							Tuple      		=		get_encoder_layer_array(lowerCamelCase_							, '_output_dense/bias' )

        _lowercase       :							Optional[Any]      		=		get_encoder_layer_array(lowerCamelCase_							, '_output_layer_norm/gamma' )
        _lowercase       :							Tuple      		=		get_encoder_layer_array(lowerCamelCase_							, '_output_layer_norm/beta' )

    # Embeddings
    _lowercase       :							Union[str, Any]      		=		get_encoder_array('_position_embedding_layer/embeddings' )
    _lowercase       :							Any      		=		get_encoder_array('_type_embedding_layer/embeddings' )
    _lowercase       :							str      		=		get_encoder_array('_embedding_norm_layer/gamma' )
    _lowercase       :							List[str]      		=		get_encoder_array('_embedding_norm_layer/beta' )

    # LM Head
    _lowercase       :							Any      		=		model.cls.predictions.transform

    _lowercase       :							Dict      		=		get_masked_lm_array('dense/kernel' )
    _lowercase       :							List[Any]      		=		get_masked_lm_array('dense/bias' )

    _lowercase       :							str      		=		get_masked_lm_array('layer_norm/gamma' )
    _lowercase       :							str      		=		get_masked_lm_array('layer_norm/beta' )

    _lowercase       :							Union[str, Any]      		=		get_masked_lm_array('embedding_table' )

    # Pooling
    _lowercase       :							List[Any]      		=		BertPooler(config=lowerCamelCase_ )
    _lowercase       :							BertPooler      		=		get_encoder_array('_pooler_layer/kernel' )
    _lowercase       :							BertPooler      		=		get_encoder_array('_pooler_layer/bias' )

    # Export final model
    model.save_pretrained(lowerCamelCase_ )

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

    print('Model conversion was done sucessfully!' )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= 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.",
 )
 SCREAMING_SNAKE_CASE       :      str												= parser.parse_args()
 convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """distilbert"""
    __lowerCamelCase						:       List[Any]														=     {
        """hidden_size""": """dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
    }





    def __init__(      self						,   a=3_0522						,   a=512						,   a=False						,   a=6						,   a=12						,   a=768						,   a=4 * 768						,   a=0.1						,   a=0.1						,   a="gelu"						,   a=0.02						,   a=0.1						,   a=0.2						,   a=0						,   **a						,   ):
         lowercase__    :   int      =       vocab_size
         lowercase__    :   int      =       max_position_embeddings
         lowercase__    :   List[str]      =       sinusoidal_pos_embds
         lowercase__    :   Tuple      =       n_layers
         lowercase__    :   Optional[int]      =       n_heads
         lowercase__    :   List[Any]      =       dim
         lowercase__    :   Any      =       hidden_dim
         lowercase__    :   Union[str, Any]      =       dropout
         lowercase__    :   Optional[int]      =       attention_dropout
         lowercase__    :   List[Any]      =       activation
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Dict      =       qa_dropout
         lowercase__    :   Tuple      =       seq_classif_dropout
         super().__init__(**a						,   pad_token_id=a)



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @property
    def       snake_case_				(      self):
         if self.task == "multiple-choice":
              lowercase__    :   List[str]      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__    :   Tuple      =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

