



"""simple docstring"""

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 __lowercase    (							_a	,    _a	,    _a					):
    def get_masked_lm_array(_a					):
        snake_case_				:  Tuple       =			f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Dict       =			tf.train.load_variable(_a	,    _a					)

        if "kernel" in name:
            snake_case_				:  Optional[int]       =			array.transpose()

        return torch.from_numpy(_a					)

    def get_encoder_array(_a					):
        snake_case_				:  Tuple       =			f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  str       =			tf.train.load_variable(_a	,    _a					)

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

        return torch.from_numpy(_a					)

    def get_encoder_layer_array(_a	,    _a					):
        snake_case_				:  Any       =			f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Union[str, Any]       =			tf.train.load_variable(_a	,    _a					)

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

        return torch.from_numpy(_a					)

    def get_encoder_attention_layer_array(_a	,    _a	,    _a					):
        snake_case_				:  int       =			f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Optional[int]       =			tf.train.load_variable(_a	,    _a					)
        snake_case_				:  int       =			array.reshape(_a					)

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

        return torch.from_numpy(_a					)

    print(f"Loading model based on config from {config_path}..."					)
    snake_case_				:  List[Any]       =			BertConfig.from_json_file(_a					)
    snake_case_				:  List[str]       =			BertForMaskedLM(_a					)

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

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

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

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

        snake_case_				:  int       =			get_encoder_attention_layer_array(
            _a	,    '''_output_dense/kernel'''	,    self_output.dense.weight.data.shape					)
        snake_case_				:  List[Any]       =			get_encoder_attention_layer_array(
            _a	,    '''_output_dense/bias'''	,    self_output.dense.bias.data.shape					)

        snake_case_				:  Union[str, Any]       =			get_encoder_layer_array(_a	,    '''_attention_layer_norm/gamma'''					)
        snake_case_				:  List[str]       =			get_encoder_layer_array(_a	,    '''_attention_layer_norm/beta'''					)

        # Intermediate
        snake_case_				:  BertIntermediate       =			layer.intermediate

        snake_case_				:  str       =			get_encoder_layer_array(_a	,    '''_intermediate_dense/kernel'''					)
        snake_case_				:  Any       =			get_encoder_layer_array(_a	,    '''_intermediate_dense/bias'''					)

        # Output
        snake_case_				:  BertOutput       =			layer.output

        snake_case_				:  int       =			get_encoder_layer_array(_a	,    '''_output_dense/kernel'''					)
        snake_case_				:  Dict       =			get_encoder_layer_array(_a	,    '''_output_dense/bias'''					)

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

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

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

    snake_case_				:  Optional[Any]       =			get_masked_lm_array('''dense/kernel'''					)
    snake_case_				:  str       =			get_masked_lm_array('''dense/bias'''					)

    snake_case_				:  List[str]       =			get_masked_lm_array('''layer_norm/gamma'''					)
    snake_case_				:  Any       =			get_masked_lm_array('''layer_norm/beta'''					)

    snake_case_				:  Dict       =			get_masked_lm_array('''embedding_table'''					)

    # Pooling
    snake_case_				:  Optional[int]       =			BertPooler(config=_a					)
    snake_case_				:  BertPooler       =			get_encoder_array('''_pooler_layer/kernel'''					)
    snake_case_				:  BertPooler       =			get_encoder_array('''_pooler_layer/bias'''					)

    # Export final model
    model.save_pretrained(_a					)

    # Integration test - should load without any errors ;)
    snake_case_				:  int       =			BertForMaskedLM.from_pretrained(_a					)
    print(new_model.eval()					)

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


if __name__ == "__main__":
     lowercase__					:						str							=			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__					:						int							=			parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





