



"""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       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: str					):
							def get_masked_lm_array(_lowerCamelCase				: str					):
														__a		:		List[str]									=       F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														__a		:		List[Any]									=       tf.train.load_variable(_lowerCamelCase  ,       _lowerCamelCase					)

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

														return torch.from_numpy(_lowerCamelCase					)

							def get_encoder_array(_lowerCamelCase				: str					):
														__a		:		Dict									=       F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														__a		:		str									=       tf.train.load_variable(_lowerCamelCase  ,       _lowerCamelCase					)

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

														return torch.from_numpy(_lowerCamelCase					)

							def get_encoder_layer_array(_lowerCamelCase				: int  ,       _lowerCamelCase				: str					):
														__a		:		int									=       F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														__a		:		Dict									=       tf.train.load_variable(_lowerCamelCase  ,       _lowerCamelCase					)

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

														return torch.from_numpy(_lowerCamelCase					)

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

														if "kernel" in name:
																					__a		:		List[Any]									=       array.transpose()

														return torch.from_numpy(_lowerCamelCase					)

							print(F'''Loading model based on config from {config_path}...'''					)
							__a		:		Tuple									=       BertConfig.from_json_file(_lowerCamelCase					)
							__a		:		Union[str, Any]									=       BertForMaskedLM(_lowerCamelCase					)

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

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

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

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

														__a		:		List[str]									=       get_encoder_attention_layer_array(
														    _lowerCamelCase  ,       """_output_dense/kernel"""  ,       self_output.dense.weight.data.shape					)
														__a		:		List[Any]									=       get_encoder_attention_layer_array(
														    _lowerCamelCase  ,       """_output_dense/bias"""  ,       self_output.dense.bias.data.shape					)

														__a		:		Optional[Any]									=       get_encoder_layer_array(_lowerCamelCase  ,       """_attention_layer_norm/gamma"""					)
														__a		:		Tuple									=       get_encoder_layer_array(_lowerCamelCase  ,       """_attention_layer_norm/beta"""					)

														# Intermediate
														__a		:		BertIntermediate									=       layer.intermediate

														__a		:		Union[str, Any]									=       get_encoder_layer_array(_lowerCamelCase  ,       """_intermediate_dense/kernel"""					)
														__a		:		str									=       get_encoder_layer_array(_lowerCamelCase  ,       """_intermediate_dense/bias"""					)

														# Output
														__a		:		BertOutput									=       layer.output

														__a		:		int									=       get_encoder_layer_array(_lowerCamelCase  ,       """_output_dense/kernel"""					)
														__a		:		List[str]									=       get_encoder_layer_array(_lowerCamelCase  ,       """_output_dense/bias"""					)

														__a		:		Tuple									=       get_encoder_layer_array(_lowerCamelCase  ,       """_output_layer_norm/gamma"""					)
														__a		:		Optional[Any]									=       get_encoder_layer_array(_lowerCamelCase  ,       """_output_layer_norm/beta"""					)

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

							# LM Head
							__a		:		Union[str, Any]									=       model.cls.predictions.transform

							__a		:		str									=       get_masked_lm_array("""dense/kernel"""					)
							__a		:		str									=       get_masked_lm_array("""dense/bias"""					)

							__a		:		Union[str, Any]									=       get_masked_lm_array("""layer_norm/gamma"""					)
							__a		:		List[Any]									=       get_masked_lm_array("""layer_norm/beta"""					)

							__a		:		Union[str, Any]									=       get_masked_lm_array("""embedding_table"""					)

							# Pooling
							__a		:		Optional[Any]									=       BertPooler(config=_lowerCamelCase					)
							__a		:		BertPooler									=       get_encoder_array("""_pooler_layer/kernel"""					)
							__a		:		BertPooler									=       get_encoder_array("""_pooler_layer/bias"""					)

							# Export final model
							model.save_pretrained(_lowerCamelCase					)

							# Integration test - should load without any errors ;)
							__a		:		Tuple									=       BertForMaskedLM.from_pretrained(_lowerCamelCase					)
							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)






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float(moles / volume					) * nfactor					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (volume)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

