






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_ (    __snake_case     :							str	,	__snake_case     :							str	,	__snake_case     :							str      ):





				'''simple docstring'''


				def get_masked_lm_array(__snake_case     :							str      ):
								snake_case__      :Tuple         =   F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								snake_case__      :Tuple         =   tf.train.load_variable(__snake_case	,	__snake_case      )

								if "kernel" in name:
												snake_case__      :List[str]         =   array.transpose()

								return torch.from_numpy(__snake_case      )

				def get_encoder_array(__snake_case     :							str      ):
								snake_case__      :Optional[Any]         =   F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								snake_case__      :Union[str, Any]         =   tf.train.load_variable(__snake_case	,	__snake_case      )

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

								return torch.from_numpy(__snake_case      )

				def get_encoder_layer_array(__snake_case     :							int	,	__snake_case     :							str      ):
								snake_case__      :Dict         =   F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								snake_case__      :Optional[int]         =   tf.train.load_variable(__snake_case	,	__snake_case      )

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

								return torch.from_numpy(__snake_case      )

				def get_encoder_attention_layer_array(__snake_case     :							int	,	__snake_case     :							str	,	__snake_case     :							Dict      ):
								snake_case__      :List[Any]         =   F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
								snake_case__      :Optional[Any]         =   tf.train.load_variable(__snake_case	,	__snake_case      )
								snake_case__      :Union[str, Any]         =   array.reshape(__snake_case      )

								if "kernel" in name:
												snake_case__      :Optional[Any]         =   array.transpose()

								return torch.from_numpy(__snake_case      )

				print(F'Loading model based on config from {config_path}...'      )
				snake_case__      :Optional[Any]         =   BertConfig.from_json_file(__snake_case      )
				snake_case__      :Any         =   BertForMaskedLM(__snake_case      )

				# 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__      :Union[str, Any]         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_query_dense/kernel"	,	self_attn.query.weight.data.shape      )
								snake_case__      :Any         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_query_dense/bias"	,	self_attn.query.bias.data.shape      )
								snake_case__      :Optional[Any]         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_key_dense/kernel"	,	self_attn.key.weight.data.shape      )
								snake_case__      :Tuple         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_key_dense/bias"	,	self_attn.key.bias.data.shape      )
								snake_case__      :Any         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_value_dense/kernel"	,	self_attn.value.weight.data.shape      )
								snake_case__      :Optional[Any]         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_value_dense/bias"	,	self_attn.value.bias.data.shape      )

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

								snake_case__      :Optional[Any]         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_output_dense/kernel"	,	self_output.dense.weight.data.shape      )
								snake_case__      :Tuple         =   get_encoder_attention_layer_array(
								    __snake_case	,	"_output_dense/bias"	,	self_output.dense.bias.data.shape      )

								snake_case__      :Any         =   get_encoder_layer_array(__snake_case	,	"_attention_layer_norm/gamma"      )
								snake_case__      :Dict         =   get_encoder_layer_array(__snake_case	,	"_attention_layer_norm/beta"      )

								# Intermediate
								snake_case__      :BertIntermediate         =   layer.intermediate

								snake_case__      :Optional[int]         =   get_encoder_layer_array(__snake_case	,	"_intermediate_dense/kernel"      )
								snake_case__      :Union[str, Any]         =   get_encoder_layer_array(__snake_case	,	"_intermediate_dense/bias"      )

								# Output
								snake_case__      :BertOutput         =   layer.output

								snake_case__      :Any         =   get_encoder_layer_array(__snake_case	,	"_output_dense/kernel"      )
								snake_case__      :Any         =   get_encoder_layer_array(__snake_case	,	"_output_dense/bias"      )

								snake_case__      :Any         =   get_encoder_layer_array(__snake_case	,	"_output_layer_norm/gamma"      )
								snake_case__      :Any         =   get_encoder_layer_array(__snake_case	,	"_output_layer_norm/beta"      )

				# Embeddings
				snake_case__      :Dict         =   get_encoder_array("_position_embedding_layer/embeddings"      )
				snake_case__      :Any         =   get_encoder_array("_type_embedding_layer/embeddings"      )
				snake_case__      :Dict         =   get_encoder_array("_embedding_norm_layer/gamma"      )
				snake_case__      :List[str]         =   get_encoder_array("_embedding_norm_layer/beta"      )

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

				snake_case__      :List[str]         =   get_masked_lm_array("dense/kernel"      )
				snake_case__      :Optional[int]         =   get_masked_lm_array("dense/bias"      )

				snake_case__      :List[Any]         =   get_masked_lm_array("layer_norm/gamma"      )
				snake_case__      :Optional[Any]         =   get_masked_lm_array("layer_norm/beta"      )

				snake_case__      :Optional[Any]         =   get_masked_lm_array("embedding_table"      )

				# Pooling
				snake_case__      :Union[str, Any]         =   BertPooler(config=__snake_case      )
				snake_case__      :BertPooler         =   get_encoder_array("_pooler_layer/kernel"      )
				snake_case__      :BertPooler         =   get_encoder_array("_pooler_layer/bias"      )

				# Export final model
				model.save_pretrained(__snake_case      )

				# Integration test - should load without any errors ;)
				snake_case__      :str         =   BertForMaskedLM.from_pretrained(__snake_case      )
				print(new_model.eval()      )

				print("Model conversion was done sucessfully!"      )


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







def 					lowercase_ (    __snake_case     :							int = 10_00      )     -> int:





				'''simple docstring'''


				snake_case__      :int         =   3
				snake_case__      :int         =   0
				while a < n:
								if a % 3 == 0 or a % 5 == 0:
												result += a
								elif a % 15 == 0:
												result -= a
								a += 1
				return result


if __name__ == "__main__":
	print(F'''{solution() = }''')