



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_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	def get_masked_lm_array(_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		__lowercase		       =			tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

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

		return torch.from_numpy(_SCREAMING_SNAKE_CASE   )

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

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

		return torch.from_numpy(_SCREAMING_SNAKE_CASE   )

	def get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		__lowercase		       =			tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

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

		return torch.from_numpy(_SCREAMING_SNAKE_CASE   )

	def get_encoder_attention_layer_array(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		__lowercase		       =			tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			array.reshape(_SCREAMING_SNAKE_CASE   )

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

		return torch.from_numpy(_SCREAMING_SNAKE_CASE   )

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

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

		# Self-attention
		__lowercase		       =			layer.attention.self

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

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

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

		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_attention_layer_norm/gamma"   )
		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_attention_layer_norm/beta"   )

		# Intermediate
		__lowercase		       =			layer.intermediate

		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_intermediate_dense/kernel"   )
		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_intermediate_dense/bias"   )

		# Output
		__lowercase		       =			layer.output

		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_dense/kernel"   )
		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_dense/bias"   )

		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_layer_norm/gamma"   )
		__lowercase		       =			get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_layer_norm/beta"   )

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

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

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

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

	__lowercase		       =			get_masked_lm_array("embedding_table"   )

	# Pooling
	__lowercase		       =			BertPooler(config=_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			get_encoder_array("_pooler_layer/kernel"   )
	__lowercase		       =			get_encoder_array("_pooler_layer/bias"   )

	# Export final model
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )

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

	print("Model conversion was done sucessfully!"   )


if __name__ == "__main__":
	snake_case__					:     Optional[Any]    		=   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__					:     Any    		=   parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








from __future__ import annotations

from typing import Any

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			num_of_nodes
							__lowercase		       =			[]
							__lowercase		       =			{}



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							self.m_edges.append([u_node, v_node, weight]	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] == u_node:
								return u_node
							return self.find_component(self.m_component[u_node]	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] != u_node:
								for k in self.m_component:
									__lowercase		       =			self.find_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							list[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if component_size[u_node] <= component_size[v_node]:
								__lowercase		       =			v_node
								component_size[v_node] += component_size[u_node]
								self.set_component(lowerCamelCase	)

							elif component_size[u_node] >= component_size[v_node]:
								__lowercase		       =			self.find_component(lowerCamelCase	)
								component_size[u_node] += component_size[v_node]
								self.set_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			[]
							__lowercase		       =			0

							__lowercase		       =			[-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes	):
								self.m_component.update({node: node}	)
								component_size.append(1	)

							__lowercase		       =			self.m_num_of_nodes

							while num_of_components > 1:
								for edge in self.m_edges:
									__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

									__lowercase		       =			self.m_component[u]
									__lowercase		       =			self.m_component[v]

									if u_component != v_component:

										for component in (u_component, v_component):
											if (
											    minimum_weight_edge[component] == -1
											    or minimum_weight_edge[component][2] > w
											):
												__lowercase		       =			[u, v, w]

								for edge in minimum_weight_edge:
									if isinstance(lowerCamelCase							,      lowerCamelCase	):
										__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

										__lowercase		       =			self.m_component[u]
										__lowercase		       =			self.m_component[v]

										if u_component != v_component:
											mst_weight += w
											self.union(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
											print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
											num_of_components -= 1

								__lowercase		       =			[-1] * self.m_num_of_nodes
							print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""	)



def    snake_case_				(    ):
	pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()



