


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		(		_lowercase					,  _lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     def get_masked_lm_array(_lowercase ):
          __UpperCamelCase =			f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __UpperCamelCase =			tf.train.load_variable(_lowercase					,  _lowercase )

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

          return torch.from_numpy(_lowercase )

     def get_encoder_array(_lowercase ):
          __UpperCamelCase =			f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __UpperCamelCase =			tf.train.load_variable(_lowercase					,  _lowercase )

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

          return torch.from_numpy(_lowercase )

     def get_encoder_layer_array(_lowercase					,  _lowercase ):
          __UpperCamelCase =			f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __UpperCamelCase =			tf.train.load_variable(_lowercase					,  _lowercase )

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

          return torch.from_numpy(_lowercase )

     def get_encoder_attention_layer_array(_lowercase					,  _lowercase					,  _lowercase ):
          __UpperCamelCase =			f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
          __UpperCamelCase =			tf.train.load_variable(_lowercase					,  _lowercase )
          __UpperCamelCase =			array.reshape(_lowercase )

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

          return torch.from_numpy(_lowercase )

     print(f'''Loading model based on config from {config_path}...''' )
     __UpperCamelCase =			BertConfig.from_json_file(_lowercase )
     __UpperCamelCase =			BertForMaskedLM(_lowercase )

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

          # Self-attention
          __UpperCamelCase =			layer.attention.self

          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_query_dense/kernel'					,  self_attn.query.weight.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_query_dense/bias'					,  self_attn.query.bias.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_key_dense/kernel'					,  self_attn.key.weight.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_key_dense/bias'					,  self_attn.key.bias.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_value_dense/kernel'					,  self_attn.value.weight.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_value_dense/bias'					,  self_attn.value.bias.data.shape )

          # Self-attention Output
          __UpperCamelCase =			layer.attention.output

          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_output_dense/kernel'					,  self_output.dense.weight.data.shape )
          __UpperCamelCase =			get_encoder_attention_layer_array(
              _lowercase					,  '_output_dense/bias'					,  self_output.dense.bias.data.shape )

          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_attention_layer_norm/gamma' )
          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_attention_layer_norm/beta' )

          # Intermediate
          __UpperCamelCase =			layer.intermediate

          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_intermediate_dense/kernel' )
          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_intermediate_dense/bias' )

          # Output
          __UpperCamelCase =			layer.output

          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_output_dense/kernel' )
          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_output_dense/bias' )

          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_output_layer_norm/gamma' )
          __UpperCamelCase =			get_encoder_layer_array(_lowercase					,  '_output_layer_norm/beta' )

     # Embeddings
     __UpperCamelCase =			get_encoder_array('_position_embedding_layer/embeddings' )
     __UpperCamelCase =			get_encoder_array('_type_embedding_layer/embeddings' )
     __UpperCamelCase =			get_encoder_array('_embedding_norm_layer/gamma' )
     __UpperCamelCase =			get_encoder_array('_embedding_norm_layer/beta' )

     # LM Head
     __UpperCamelCase =			model.cls.predictions.transform

     __UpperCamelCase =			get_masked_lm_array('dense/kernel' )
     __UpperCamelCase =			get_masked_lm_array('dense/bias' )

     __UpperCamelCase =			get_masked_lm_array('layer_norm/gamma' )
     __UpperCamelCase =			get_masked_lm_array('layer_norm/beta' )

     __UpperCamelCase =			get_masked_lm_array('embedding_table' )

     # Pooling
     __UpperCamelCase =			BertPooler(config=_lowercase )
     __UpperCamelCase =			get_encoder_array('_pooler_layer/kernel' )
     __UpperCamelCase =			get_encoder_array('_pooler_layer/bias' )

     # Export final model
     model.save_pretrained(_lowercase )

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

     print('Model conversion was done sucessfully!' )


if __name__ == "__main__":
    __snake_case   							=						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.''',
    )
    __snake_case   							=						parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import pytest

import datasets


# Import fixture modules as plugins
__snake_case   							=						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['integration', 'unit'] ):
               continue
          item.add_marker(pytest.mark.unit )






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     config.addinivalue_line('markers'					,  'torchaudio_latest: mark test to run with torchaudio>=0.12' )






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			tmp_path_factory.getbasetemp() / 'cache'
     __UpperCamelCase =			test_hf_cache_home / 'datasets'
     __UpperCamelCase =			test_hf_cache_home / 'metrics'
     __UpperCamelCase =			test_hf_cache_home / 'modules'
     monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'					,  str(_lowercase ) )
     monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads'
     monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'					,  str(_lowercase ) )
     __UpperCamelCase =			test_hf_datasets_cache / 'downloads' / 'extracted'
     monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'					,  str(_lowercase ) )






@pytest.fixture(autouse=_lowercase					,  scope='session' )
def 		_A		(		)					-> Dict:


     """simple docstring"""


     datasets.disable_progress_bar()






@pytest.fixture(autouse=_lowercase )
def 		_A		(		_lowercase )					-> Tuple:


     """simple docstring"""


     monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'					,  _lowercase )






@pytest.fixture
def 		_A		(		_lowercase )					-> Any:


     """simple docstring"""


     monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'					,  _lowercase )


