
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 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	def get_masked_lm_array(UpperCamelCase:     str  ):
		__lowerCAmelCase         =							F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		__lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

		return torch.from_numpy(UpperCamelCase  )

	def get_encoder_array(UpperCamelCase:     str  ):
		__lowerCAmelCase         =							F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		__lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

		return torch.from_numpy(UpperCamelCase  )

	def get_encoder_layer_array(UpperCamelCase:     int			,							UpperCamelCase:     str  ):
		__lowerCAmelCase         =							F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		__lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

		return torch.from_numpy(UpperCamelCase  )

	def get_encoder_attention_layer_array(UpperCamelCase:     int			,							UpperCamelCase:     str			,							UpperCamelCase:     Any  ):
		__lowerCAmelCase         =							F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
		__lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )
		__lowerCAmelCase         =							array.reshape(UpperCamelCase  )

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

		return torch.from_numpy(UpperCamelCase  )

	print(F"Loading model based on config from {config_path}..."  )
	__lowerCAmelCase         =							BertConfig.from_json_file(UpperCamelCase  )
	__lowerCAmelCase         =							BertForMaskedLM(UpperCamelCase  )

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

		# Self-attention
		__lowerCAmelCase         =							layer.attention.self

		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_query_dense/kernel"			,							self_attn.query.weight.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_query_dense/bias"			,							self_attn.query.bias.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_key_dense/kernel"			,							self_attn.key.weight.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_key_dense/bias"			,							self_attn.key.bias.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_value_dense/kernel"			,							self_attn.value.weight.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_value_dense/bias"			,							self_attn.value.bias.data.shape  )

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

		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_output_dense/kernel"			,							self_output.dense.weight.data.shape  )
		__lowerCAmelCase         =							get_encoder_attention_layer_array(
		    UpperCamelCase			,							"_output_dense/bias"			,							self_output.dense.bias.data.shape  )

		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_attention_layer_norm/gamma"  )
		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_attention_layer_norm/beta"  )

		# Intermediate
		__lowerCAmelCase         =							layer.intermediate

		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_intermediate_dense/kernel"  )
		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_intermediate_dense/bias"  )

		# Output
		__lowerCAmelCase         =							layer.output

		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_dense/kernel"  )
		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_dense/bias"  )

		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_layer_norm/gamma"  )
		__lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_layer_norm/beta"  )

	# Embeddings
	__lowerCAmelCase         =							get_encoder_array("_position_embedding_layer/embeddings"  )
	__lowerCAmelCase         =							get_encoder_array("_type_embedding_layer/embeddings"  )
	__lowerCAmelCase         =							get_encoder_array("_embedding_norm_layer/gamma"  )
	__lowerCAmelCase         =							get_encoder_array("_embedding_norm_layer/beta"  )

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

	__lowerCAmelCase         =							get_masked_lm_array("dense/kernel"  )
	__lowerCAmelCase         =							get_masked_lm_array("dense/bias"  )

	__lowerCAmelCase         =							get_masked_lm_array("layer_norm/gamma"  )
	__lowerCAmelCase         =							get_masked_lm_array("layer_norm/beta"  )

	__lowerCAmelCase         =							get_masked_lm_array("embedding_table"  )

	# Pooling
	__lowerCAmelCase         =							BertPooler(config=UpperCamelCase  )
	__lowerCAmelCase         =							get_encoder_array("_pooler_layer/kernel"  )
	__lowerCAmelCase         =							get_encoder_array("_pooler_layer/bias"  )

	# Export final model
	model.save_pretrained(UpperCamelCase  )

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

	print("Model conversion was done sucessfully!"  )


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








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							data
								__lowerCAmelCase         =							None

class       a		:







							def __init__(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							None







							def __iter__(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.head
								while self.head:
									yield node.data
									__lowerCAmelCase         =							node.next
									if node == self.head:
										break







							def __len__(						self  :  Dict ):
								"""simple docstring"""







								return sum(1 for _ in self )







							def __repr__(						self  :  str ):
								"""simple docstring"""







								return "->".join(str(snake_case__ ) for item in iter(self ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(len(self )      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(0      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Any ):
								"""simple docstring"""







								if index < 0 or index > len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							Node(snake_case__ )
								if self.head is None:
									__lowerCAmelCase         =							new_node  # first node points itself
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								elif index == 0:  # insert at head
									__lowerCAmelCase         =							self.head
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							new_node
									if index == len(self ) - 1:  # insert at tail
										__lowerCAmelCase         =							new_node







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								return self.delete_nth(0 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.delete_nth(len(self ) - 1 )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int = 0 ):
								"""simple docstring"""







								if not 0 <= index < len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							self.head
								if self.head == self.tail:  # just one node
									__lowerCAmelCase         =							__lowerCAmelCase         =							None
								elif index == 0:  # delete head node
									__lowerCAmelCase         =							self.tail.next.next
									__lowerCAmelCase         =							self.head.next
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next.next
									if index == len(self ) - 1:  # delete at tail
										__lowerCAmelCase         =							temp
								return delete_node.data







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return len(self ) == 0






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase			,							i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0			,							7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2			,							3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()






