
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      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str       )  ->    int:
							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__				:      Tuple       ):
														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__":
		lowercase        =  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.''',
		)
		lowercase        =  parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float(moles / volume       ) * nfactor       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (volume)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((pressure * volume) / (0.0_8_2_1 * moles)       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





