





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_	(		A__		,      A__		,      A__				)  ->		int:
       def get_masked_lm_array(A__				):
              a__						:	Dict =				F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              a__						:	List[str] =				tf.train.load_variable(A__		,      A__				)

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

              return torch.from_numpy(A__				)

       def get_encoder_array(A__				):
              a__						:	Dict =				F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              a__						:	str =				tf.train.load_variable(A__		,      A__				)

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

              return torch.from_numpy(A__				)

       def get_encoder_layer_array(A__		,      A__				):
              a__						:	str =				F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              a__						:	List[str] =				tf.train.load_variable(A__		,      A__				)

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

              return torch.from_numpy(A__				)

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

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

              return torch.from_numpy(A__				)

       print(F'Loading model based on config from {config_path}...'				)
       a__						:	List[str] =				BertConfig.from_json_file(A__				)
       a__						:	str =				BertForMaskedLM(A__				)

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

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

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

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

              a__						:	Optional[Any] =				get_encoder_attention_layer_array(
                  A__		,      '_output_dense/kernel'		,      self_output.dense.weight.data.shape				)
              a__						:	Any =				get_encoder_attention_layer_array(
                  A__		,      '_output_dense/bias'		,      self_output.dense.bias.data.shape				)

              a__						:	str =				get_encoder_layer_array(A__		,      '_attention_layer_norm/gamma'				)
              a__						:	Optional[Any] =				get_encoder_layer_array(A__		,      '_attention_layer_norm/beta'				)

              # Intermediate
              a__						:	BertIntermediate =				layer.intermediate

              a__						:	Tuple =				get_encoder_layer_array(A__		,      '_intermediate_dense/kernel'				)
              a__						:	Tuple =				get_encoder_layer_array(A__		,      '_intermediate_dense/bias'				)

              # Output
              a__						:	BertOutput =				layer.output

              a__						:	Optional[Any] =				get_encoder_layer_array(A__		,      '_output_dense/kernel'				)
              a__						:	List[str] =				get_encoder_layer_array(A__		,      '_output_dense/bias'				)

              a__						:	Optional[int] =				get_encoder_layer_array(A__		,      '_output_layer_norm/gamma'				)
              a__						:	Optional[int] =				get_encoder_layer_array(A__		,      '_output_layer_norm/beta'				)

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

       # LM Head
       a__						:	Tuple =				model.cls.predictions.transform

       a__						:	List[Any] =				get_masked_lm_array('dense/kernel'				)
       a__						:	Tuple =				get_masked_lm_array('dense/bias'				)

       a__						:	List[str] =				get_masked_lm_array('layer_norm/gamma'				)
       a__						:	Optional[int] =				get_masked_lm_array('layer_norm/beta'				)

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

       # Pooling
       a__						:	Any =				BertPooler(config=A__				)
       a__						:	BertPooler =				get_encoder_array('_pooler_layer/kernel'				)
       a__						:	BertPooler =				get_encoder_array('_pooler_layer/bias'				)

       # Export final model
       model.save_pretrained(A__				)

       # Integration test - should load without any errors ;)
       a__						:	Any =				BertForMaskedLM.from_pretrained(A__				)
       print(new_model.eval()				)

       print('Model conversion was done sucessfully!'				)


if __name__ == "__main__":
    lowercase      :       Optional[int]									=  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.""",
    )
    lowercase      :       Optional[int]									=  parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			Optional[NestedDataStructureLike[PathLike]] = None		,      __lowercase					:			Optional[NamedSplit] = None		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			List[str]		,      )							->      Tuple:
        __UpperCAmelCase							:	Any           =  path_or_paths
        __UpperCAmelCase							:	Dict           =  split if split or isinstance(__lowercase		,      __lowercase					) else """train"""
        __UpperCAmelCase							:	Optional[int]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	str           =  keep_in_memory
        __UpperCAmelCase							:	Optional[int]           =  streaming
        __UpperCAmelCase							:	Dict           =  num_proc
        __UpperCAmelCase							:	Tuple           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
        pass


class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			Optional[Any]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	Optional[Any]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	Optional[int]           =  keep_in_memory
        __UpperCAmelCase							:	Dict           =  streaming
        __UpperCAmelCase							:	Optional[Any]           =  num_proc
        __UpperCAmelCase							:	Union[str, Any]           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Union[Dataset, IterableDataset]:
        pass


