





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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    def get_masked_lm_array(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
        SCREAMING_SNAKE_CASE__											=			tf.train.load_variable(lowerCAmelCase_ ,							lowerCAmelCase_	)

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

        return torch.from_numpy(lowerCAmelCase_	)

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

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

        return torch.from_numpy(lowerCAmelCase_	)

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

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

        return torch.from_numpy(lowerCAmelCase_	)

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

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

        return torch.from_numpy(lowerCAmelCase_	)

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

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

        # Self-attention
        SCREAMING_SNAKE_CASE__											=			layer.attention.self

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

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

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

        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_attention_layer_norm/gamma'''	)
        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_attention_layer_norm/beta'''	)

        # Intermediate
        SCREAMING_SNAKE_CASE__											=			layer.intermediate

        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_intermediate_dense/kernel'''	)
        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_intermediate_dense/bias'''	)

        # Output
        SCREAMING_SNAKE_CASE__											=			layer.output

        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_output_dense/kernel'''	)
        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_output_dense/bias'''	)

        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_output_layer_norm/gamma'''	)
        SCREAMING_SNAKE_CASE__											=			get_encoder_layer_array(lowerCAmelCase_ ,							'''_output_layer_norm/beta'''	)

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

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

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

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

    SCREAMING_SNAKE_CASE__											=			get_masked_lm_array('''embedding_table'''	)

    # Pooling
    SCREAMING_SNAKE_CASE__											=			BertPooler(config=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			get_encoder_array('''_pooler_layer/kernel'''	)
    SCREAMING_SNAKE_CASE__											=			get_encoder_array('''_pooler_layer/bias'''	)

    # Export final model
    model.save_pretrained(lowerCAmelCase_	)

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

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


if __name__ == "__main__":
  _A					:				List[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.""",
  )
  _A					:				str				=			parser.parse_args()
  convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def    _snake_case  (	)		->       Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self  )			->				int:
														super().__init__()
														lowerCAmelCase__			  =      nn.Linear(3   ,       4  )
														lowerCAmelCase__			  =      nn.BatchNormad(4  )
														lowerCAmelCase__			  =      nn.Linear(4   ,       5  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      mock_training_loop_function('''hello'''  )
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )
														self.assertListEqual([bs, arga]   ,       [8, '''hello''']  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														@find_executable_batch_size(starting_batch_size=0  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					if batch_size > 0:
																												raise_fake_out_of_memory()
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function(1_28   ,       '''hello'''   ,       '''world'''  )
																					self.assertIn('''Batch size was passed into `f`'''   ,       cm.exception.args[0]  )
																					self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					raise ValueError('''Oops, we had an error!'''  )

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''Oops, we had an error!'''   ,       cm.exception.args[0]  )







							@require_cuda
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.cuda.memory_allocated()
														lowerCAmelCase__			  =      ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      release_memory(lowerCamelCase_  )
														self.assertEqual(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )