



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)








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The length of profit and weight must be same."   )
	if max_weight <= 0:
		raise ValueError("max_weight must greater than zero."   )
	if any(p < 0 for p in profit   ):
		raise ValueError("Profit can not be negative."   )
	if any(w < 0 for w in weight   ):
		raise ValueError("Weight can not be negative."   )

	# List created to store profit gained for the 1kg in case of each weight
	# respectively.  Calculate and append profit/weight for each element.
	__lowercase		       =			[p / w for p, w in zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )]

	# Creating a copy of the list and sorting profit/weight in ascending order
	__lowercase		       =			sorted(_SCREAMING_SNAKE_CASE   )

	# declaring useful variables
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			0
	__lowercase		       =			0
	__lowercase		       =			0

	# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
	while limit <= max_weight and i < length:
		# flag value for encountered greatest element in sorted_profit_by_weight
		__lowercase		       =			sorted_profit_by_weight[length - i - 1]
		__lowercase		       =			profit_by_weight.index(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			-1

		# check if the weight encountered is less than the total weight
		# encountered before.
		if max_weight - limit >= weight[index]:
			limit += weight[index]
			# Adding profit gained for the given weight 1 ===
			# weight[index]/weight[index]
			gain += 1 * profit[index]
		else:
			# Since the weight encountered is greater than limit, therefore take the
			# required number of remaining kgs and calculate profit for it.
			# weight remaining / weight[index]
			gain += (max_weight - limit) / weight[index] * profit[index]
			break
		i += 1
	return gain


if __name__ == "__main__":
	print(
	    """Input profits, weights, and then max_weight (all positive ints) separated by """
	    """spaces."""
	)

	snake_case__					:     str    		=   [int(x) for x in input("""Input profits separated by spaces: """).split()]
	snake_case__					:     str    		=   [int(x) for x in input("""Input weights separated by spaces: """).split()]
	snake_case__					:     Optional[Any]    		=   int(input("""Max weight allowed: """))

	# Function Call
	calc_profit(profit, weight, max_weight)



