



'''simple docstring'''





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()
SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)




def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    int  ,_snake_case	:    List[str]							):





    '''simple docstring'''


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

    # Sanity check
    if len(set(_snake_case							)							) != 1:
        raise ValueError(f'''Found layer names with different depths (layer depth {list(set(_snake_case							)							)})'''							)
    lowercase__									=    list(set(_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(_snake_case  ,_snake_case							):
        lowercase__									=    full_name.split("/"							)
        lowercase__									=    model
        lowercase__									=    []
        for i, m_name in enumerate(_snake_case							):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith("layer_with_weights"							):
                lowercase__									=    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"]							)
                    lowercase__									=    getattr(_snake_case  ,"embeddings"							)
                    lowercase__									=    getattr(_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							)]							)
                    lowercase__									=    getattr(_snake_case  ,"encoder"							)
                    lowercase__									=    getattr(_snake_case  ,"layer"							)
                    lowercase__									=    pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(["pooler", "dense"]							)
                    lowercase__									=    getattr(_snake_case  ,"pooler"							)
                    lowercase__									=    getattr(_snake_case  ,"dense"							)
            elif m_name == "embeddings":
                trace.append("embeddings"							)
                lowercase__									=    getattr(_snake_case  ,"embeddings"							)
                if layer_num == 0:
                    trace.append("word_embeddings"							)
                    lowercase__									=    getattr(_snake_case  ,"word_embeddings"							)
                elif layer_num == 1:
                    trace.append("position_embeddings"							)
                    lowercase__									=    getattr(_snake_case  ,"position_embeddings"							)
                elif layer_num == 2:
                    trace.append("token_type_embeddings"							)
                    lowercase__									=    getattr(_snake_case  ,"token_type_embeddings"							)
                else:
                    raise ValueError(f'''Unknown embedding layer with name {full_name}'''							)
                trace.append("weight"							)
                lowercase__									=    getattr(_snake_case  ,"weight"							)
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(["attention", "self"]							)
                lowercase__									=    getattr(_snake_case  ,"attention"							)
                lowercase__									=    getattr(_snake_case  ,"self"							)
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(["attention", "output", "LayerNorm"]							)
                lowercase__									=    getattr(_snake_case  ,"attention"							)
                lowercase__									=    getattr(_snake_case  ,"output"							)
                lowercase__									=    getattr(_snake_case  ,"LayerNorm"							)
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(["attention", "output", "dense"]							)
                lowercase__									=    getattr(_snake_case  ,"attention"							)
                lowercase__									=    getattr(_snake_case  ,"output"							)
                lowercase__									=    getattr(_snake_case  ,"dense"							)
            elif m_name == "_output_dense":
                # output dense
                trace.extend(["output", "dense"]							)
                lowercase__									=    getattr(_snake_case  ,"output"							)
                lowercase__									=    getattr(_snake_case  ,"dense"							)
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(["output", "LayerNorm"]							)
                lowercase__									=    getattr(_snake_case  ,"output"							)
                lowercase__									=    getattr(_snake_case  ,"LayerNorm"							)
            elif m_name == "_key_dense":
                # attention key
                trace.append("key"							)
                lowercase__									=    getattr(_snake_case  ,"key"							)
            elif m_name == "_query_dense":
                # attention query
                trace.append("query"							)
                lowercase__									=    getattr(_snake_case  ,"query"							)
            elif m_name == "_value_dense":
                # attention value
                trace.append("value"							)
                lowercase__									=    getattr(_snake_case  ,"value"							)
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(["intermediate", "dense"]							)
                lowercase__									=    getattr(_snake_case  ,"intermediate"							)
                lowercase__									=    getattr(_snake_case  ,"dense"							)
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append("output"							)
                lowercase__									=    getattr(_snake_case  ,"output"							)
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append("bias"							)
                lowercase__									=    getattr(_snake_case  ,"bias"							)
            elif m_name in ["kernel", "gamma"]:
                trace.append("weight"							)
                lowercase__									=    getattr(_snake_case  ,"weight"							)
            else:
                logger.warning(f'''Ignored {m_name}'''							)
        # for certain layers reshape is necessary
        lowercase__									=    ".".join(_snake_case							)
        if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"  ,_snake_case							) or re.match(
            R"(\S+)\.attention\.output\.dense\.weight"  ,_snake_case							):
            lowercase__									=    array.reshape(pointer.data.shape							)
        if "kernel" in full_name:
            lowercase__									=    array.transpose()
        if pointer.shape == array.shape:
            lowercase__									=    torch.from_numpy(_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 							lowerCamelCase	(					_snake_case	:    Tuple  ,_snake_case	:    Dict  ,_snake_case	:    Optional[Any]							):





    '''simple docstring'''


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

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

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


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









'''simple docstring'''





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_ = "▁"  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = "<unk>"  ,UpperCAmelCase_ = "</s>"  ,UpperCAmelCase_ = "<pad>"  ,)	->				Optional[int]:
           lowercase__									=    {
               "pad": {"id": 0, "token": pad_token},
               "eos": {"id": 1, "token": eos_token},
               "unk": {"id": 2, "token": unk_token},
           }

           lowercase__									=    [None] * len(self.special_tokens   )
           for token_dict in self.special_tokens.values():
               lowercase__									=    token_dict["token"]

           lowercase__									=    Tokenizer(Unigram()   )

           lowercase__									=    normalizers.Sequence(
               [
                   normalizers.Nmt(),
                   normalizers.NFKC(),
                   normalizers.Replace(Regex(" {2,}"   )  ," "   ),
                   normalizers.Lowercase(),
               ]   )
           lowercase__									=    pre_tokenizers.Sequence(
               [
                   pre_tokenizers.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   ),
                   pre_tokenizers.Digits(individual_digits=UpperCAmelCase_   ),
                   pre_tokenizers.Punctuation(),
               ]   )
           lowercase__									=    decoders.Metaspace(replacement=UpperCAmelCase_  ,add_prefix_space=UpperCAmelCase_   )

           lowercase__									=    TemplateProcessing(
               single=F'''$A {self.special_tokens['eos']['token']}'''  ,special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]  ,)

           lowercase__									=    {
               "model": "SentencePieceUnigram",
               "replacement": replacement,
               "add_prefix_space": add_prefix_space,
           }

           super().__init__(UpperCAmelCase_  ,UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				List[str]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               lowercase__									=    [files]
           self._tokenizer.train(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = 8_000  ,UpperCAmelCase_ = True  ,)	->				Union[str, Any]:
           lowercase__									=    trainers.UnigramTrainer(
               vocab_size=UpperCAmelCase_  ,special_tokens=self.special_tokens_list  ,show_progress=UpperCAmelCase_  ,)

           self._tokenizer.train_from_iterator(UpperCAmelCase_  ,trainer=UpperCAmelCase_   )

           self.add_unk_id()







       def 				_a   (					self   )	->				str:
           lowercase__									=    json.loads(self._tokenizer.to_str()   )

           lowercase__									=    self.special_tokens["unk"]["id"]

           lowercase__									=    Tokenizer.from_str(json.dumps(UpperCAmelCase_   )   )




