

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Dict:





    '''simple docstring'''






    A__          =   os.path.abspath(SCREAMING_SNAKE_CASE__      )
    logger.info(f'Converting TensorFlow checkpoint from {tf_path}'      )
    # Load weights from TF model
    A__          =   tf.train.list_variables(SCREAMING_SNAKE_CASE__      )
    A__          =   []
    A__          =   []
    A__          =   []
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        A__          =   full_name.split('/'      )
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(f'Skipping non-model layer {full_name}'      )
            continue
        if "optimizer" in full_name:
            logger.info(f'Skipping optimization layer {full_name}'      )
            continue
        if name[0] == "model":
            # ignore initial 'model'
            A__          =   name[1:]
        # figure out how many levels deep the name is
        A__          =   0
        for _name in name:
            if _name.startswith('layer_with_weights'      ):
                depth += 1
            else:
                break
        layer_depth.append(SCREAMING_SNAKE_CASE__      )
        # read data
        A__          =   tf.train.load_variable(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        names.append('/'.join(SCREAMING_SNAKE_CASE__      )      )
        arrays.append(SCREAMING_SNAKE_CASE__      )
    logger.info(f'Read a total of {len(SCREAMING_SNAKE_CASE__      ):,} layers'      )

    # Sanity check
    if len(set(SCREAMING_SNAKE_CASE__      )      ) != 1:
        raise ValueError(f'Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE__      )      )})'      )
    A__          =   list(set(SCREAMING_SNAKE_CASE__      )      )[0]
    if layer_depth != 1:
        raise ValueError(
            'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
            ' heads.'      )

    # convert layers
    logger.info('Converting weights...'      )
    for full_name, array in zip(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      ):
        A__          =   full_name.split('/'      )
        A__          =   model
        A__          =   []
        for i, m_name in enumerate(SCREAMING_SNAKE_CASE__      ):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('layer_with_weights'      ):
                A__          =   int(m_name.split('-'      )[-1]      )
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['embeddings', 'LayerNorm']      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'embeddings'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'LayerNorm'      )
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['encoder', 'layer', str(layer_num - 4      )]      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'encoder'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'layer'      )
                    A__          =   pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['pooler', 'dense']      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'pooler'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'dense'      )
            elif m_name == "embeddings":
                trace.append('embeddings'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'embeddings'      )
                if layer_num == 0:
                    trace.append('word_embeddings'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'word_embeddings'      )
                elif layer_num == 1:
                    trace.append('position_embeddings'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'position_embeddings'      )
                elif layer_num == 2:
                    trace.append('token_type_embeddings'      )
                    A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'token_type_embeddings'      )
                else:
                    raise ValueError(f'Unknown embedding layer with name {full_name}'      )
                trace.append('weight'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'weight'      )
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['attention', 'self']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'attention'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'self'      )
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['attention', 'output', 'LayerNorm']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'attention'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'output'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'LayerNorm'      )
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['attention', 'output', 'dense']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'attention'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'output'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'dense'      )
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['output', 'dense']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'output'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'dense'      )
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['output', 'LayerNorm']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'output'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'LayerNorm'      )
            elif m_name == "_key_dense":
                # attention key
                trace.append('key'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'key'      )
            elif m_name == "_query_dense":
                # attention query
                trace.append('query'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'query'      )
            elif m_name == "_value_dense":
                # attention value
                trace.append('value'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'value'      )
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['intermediate', 'dense']      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'intermediate'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'dense'      )
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('output'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'output'      )
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('bias'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'bias'      )
            elif m_name in ["kernel", "gamma"]:
                trace.append('weight'      )
                A__          =   getattr(SCREAMING_SNAKE_CASE__    ,			'weight'      )
            else:
                logger.warning(f'Ignored {m_name}'      )
        # for certain layers reshape is necessary
        A__          =   '.'.join(SCREAMING_SNAKE_CASE__      )
        if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'    ,			SCREAMING_SNAKE_CASE__      ) or re.match(
            R'(\S+)\.attention\.output\.dense\.weight'    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   array.reshape(pointer.data.shape      )
        if "kernel" in full_name:
            A__          =   array.transpose()
        if pointer.shape == array.shape:
            A__          =   torch.from_numpy(SCREAMING_SNAKE_CASE__      )
        else:
            raise ValueError(
                f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                f' {array.shape}'      )
        logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'      )
    return model
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Any:





    '''simple docstring'''






    logger.info(f'Loading model based on config from {config_path}...'      )
    A__          =   BertConfig.from_json_file(SCREAMING_SNAKE_CASE__      )
    A__          =   BertModel(SCREAMING_SNAKE_CASE__      )

    # Load weights from checkpoint
    logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'      )
    load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    # Save pytorch-model
    logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'      )
    torch.save(model.state_dict()    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  argparse.ArgumentParser()
     parser.add_argument(
         "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
     )
     lowercase_       		=  parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


