


'''simple docstring'''
def 	_A  (       A							,A							,A							)  ->				int:
	if len(A							) != len(A							):
		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						:		List[str]   					=		[p / w for p, w in zip(A							,A							)]

	# Creating a copy of the list and sorting profit/weight in ascending order
	lowercase						:		int   					=		sorted(A							)

	# declaring useful variables
	lowercase						:		Any   					=		len(A							)
	lowercase						:		List[Any]   					=		0
	lowercase						:		Optional[Any]   					=		0
	lowercase						:		str   					=		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						:		int   					=		sorted_profit_by_weight[length - i - 1]
		lowercase						:		Any   					=		profit_by_weight.index(A							)
		lowercase						:		Dict   					=		-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."""
					)

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

					# Function Call
					calc_profit(profit, weight, max_weight)






'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCAmelCase   : Any             =				[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]




def 	_A  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		if new_k not in sd:
			raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''							)

		if "dense" in k or "proj" in new_k:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,dtype=sd[new_k].dtype							)
		assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
	# make sure embedding.padding_idx is respected
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
	assert extra == [], F'''no matches found for the following tf keys {extra}'''
	return torch_model




def 	_A  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

	# convert model
	lowercase						:		Dict   					=		get_tf_weights_as_numpy(A							)
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']
	if dataset == "large":
		lowercase						:		str   					=		task_specific_params
	lowercase						:		List[str]   					=		convert_pegasus(A							,A							)
	torch_model.save_pretrained(A							)
	lowercase						:		Union[str, Any]   					=		torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight"							)
	sd.pop("model.encoder.embed_positions.weight"							)
	torch.save(A							,Path(A							) / "pytorch_model.bin"							)


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
					parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


