


'''simple docstring'''



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__ ,      UpperCamelCase__ ,      UpperCamelCase__					):







			def get_masked_lm_array(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					Union[str, Any]        =     tf.train.load_variable(__UpperCamelCase ,      __UpperCamelCase					)

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

						return torch.from_numpy(__UpperCamelCase					)

			def get_encoder_array(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					List[str]        =     tf.train.load_variable(__UpperCamelCase ,      __UpperCamelCase					)

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

						return torch.from_numpy(__UpperCamelCase					)

			def get_encoder_layer_array(UpperCamelCase__ ,      UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					List[str]        =     tf.train.load_variable(__UpperCamelCase ,      __UpperCamelCase					)

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

						return torch.from_numpy(__UpperCamelCase					)

			def get_encoder_attention_layer_array(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					List[Any]        =     tf.train.load_variable(__UpperCamelCase ,      __UpperCamelCase					)
						UpperCAmelCase__  :					Optional[int]        =     array.reshape(__UpperCamelCase					)

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

						return torch.from_numpy(__UpperCamelCase					)

			print(f'''Loading model based on config from {config_path}...'''					)
			UpperCAmelCase__  :					List[Any]        =     BertConfig.from_json_file(__UpperCamelCase					)
			UpperCAmelCase__  :					Tuple        =     BertForMaskedLM(__UpperCamelCase					)

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

						# Self-attention
						UpperCAmelCase__  :					BertSelfAttention        =     layer.attention.self

						UpperCAmelCase__  :					str        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_query_dense/kernel""" ,      self_attn.query.weight.data.shape					)
						UpperCAmelCase__  :					Optional[Any]        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_query_dense/bias""" ,      self_attn.query.bias.data.shape					)
						UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_key_dense/kernel""" ,      self_attn.key.weight.data.shape					)
						UpperCAmelCase__  :					Optional[Any]        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_key_dense/bias""" ,      self_attn.key.bias.data.shape					)
						UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_value_dense/kernel""" ,      self_attn.value.weight.data.shape					)
						UpperCAmelCase__  :					int        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_value_dense/bias""" ,      self_attn.value.bias.data.shape					)

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

						UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_output_dense/kernel""" ,      self_output.dense.weight.data.shape					)
						UpperCAmelCase__  :					Tuple        =     get_encoder_attention_layer_array(
						    __UpperCamelCase ,      """_output_dense/bias""" ,      self_output.dense.bias.data.shape					)

						UpperCAmelCase__  :					Tuple        =     get_encoder_layer_array(__UpperCamelCase ,      """_attention_layer_norm/gamma"""					)
						UpperCAmelCase__  :					Union[str, Any]        =     get_encoder_layer_array(__UpperCamelCase ,      """_attention_layer_norm/beta"""					)

						# Intermediate
						UpperCAmelCase__  :					BertIntermediate        =     layer.intermediate

						UpperCAmelCase__  :					Tuple        =     get_encoder_layer_array(__UpperCamelCase ,      """_intermediate_dense/kernel"""					)
						UpperCAmelCase__  :					int        =     get_encoder_layer_array(__UpperCamelCase ,      """_intermediate_dense/bias"""					)

						# Output
						UpperCAmelCase__  :					BertOutput        =     layer.output

						UpperCAmelCase__  :					Any        =     get_encoder_layer_array(__UpperCamelCase ,      """_output_dense/kernel"""					)
						UpperCAmelCase__  :					Union[str, Any]        =     get_encoder_layer_array(__UpperCamelCase ,      """_output_dense/bias"""					)

						UpperCAmelCase__  :					Optional[Any]        =     get_encoder_layer_array(__UpperCamelCase ,      """_output_layer_norm/gamma"""					)
						UpperCAmelCase__  :					Union[str, Any]        =     get_encoder_layer_array(__UpperCamelCase ,      """_output_layer_norm/beta"""					)

			# Embeddings
			UpperCAmelCase__  :					Dict        =     get_encoder_array("""_position_embedding_layer/embeddings"""					)
			UpperCAmelCase__  :					int        =     get_encoder_array("""_type_embedding_layer/embeddings"""					)
			UpperCAmelCase__  :					Any        =     get_encoder_array("""_embedding_norm_layer/gamma"""					)
			UpperCAmelCase__  :					Tuple        =     get_encoder_array("""_embedding_norm_layer/beta"""					)

			# LM Head
			UpperCAmelCase__  :					str        =     model.cls.predictions.transform

			UpperCAmelCase__  :					List[Any]        =     get_masked_lm_array("""dense/kernel"""					)
			UpperCAmelCase__  :					Tuple        =     get_masked_lm_array("""dense/bias"""					)

			UpperCAmelCase__  :					List[Any]        =     get_masked_lm_array("""layer_norm/gamma"""					)
			UpperCAmelCase__  :					str        =     get_masked_lm_array("""layer_norm/beta"""					)

			UpperCAmelCase__  :					Any        =     get_masked_lm_array("""embedding_table"""					)

			# Pooling
			UpperCAmelCase__  :					Optional[int]        =     BertPooler(config=__UpperCamelCase					)
			UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/kernel"""					)
			UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/bias"""					)

			# Export final model
			model.save_pretrained(__UpperCamelCase					)

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

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


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



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()