"""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


SCREAMING_SNAKE_CASE									=						[
    # 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      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	for pegasus_name, hf_name in PATTERNS:
		_lowercase :    Dict							=							k.replace(__UpperCAmelCase     ,__UpperCAmelCase			)
	return k


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    int							=							DEFAULTS.copy()
	cfg_kwargs.update(__UpperCAmelCase			)
	_lowercase :    Optional[int]							=							PegasusConfig(**__UpperCAmelCase			)
	_lowercase :    Union[str, Any]							=							PegasusForConditionalGeneration(__UpperCAmelCase			)
	_lowercase :    Any							=							torch_model.model.state_dict()
	_lowercase :    Any							=							{}
	for k, v in tf_weights.items():
		_lowercase :    Optional[int]							=							rename_state_dict_key(__UpperCAmelCase			)
		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 :    int							=							v.T
		_lowercase :    List[str]							=							torch.tensor(__UpperCAmelCase     ,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 :    List[Any]							=							torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]			)
	_lowercase :    int							=							mapping['shared.weight']
	_lowercase :    Union[str, Any]							=							mapping['shared.weight']
	_lowercase :    Dict							=							{k: torch.zeros_like(__UpperCAmelCase			) for k, v in sd.items() if k.endswith('bias'			) and k not in mapping}
	mapping.update(**__UpperCAmelCase			)
	_lowercase				,      _lowercase :    Union[str, Any]							=							torch_model.model.load_state_dict(__UpperCAmelCase     ,strict=__UpperCAmelCase			)
	_lowercase :    Optional[Any]							=							[
	    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      __lowerCAmelCase(		__UpperCAmelCase="./ckpt/aeslc/model.ckpt-32000"			):






	"""simple docstring"""





	_lowercase :    Dict							=							tf.train.list_variables(__UpperCAmelCase			)
	_lowercase :    Optional[Any]							=							{}
	_lowercase :    Tuple							=							['Adafactor', 'global_step']
	for name, shape in tqdm(__UpperCAmelCase     ,desc='converting tf checkpoint to dict'			):
		_lowercase :    Union[str, Any]							=							any(pat in name for pat in ignore_name			)
		if skip_key:
			continue
		_lowercase :    Optional[Any]							=							tf.train.load_variable(__UpperCAmelCase     ,__UpperCAmelCase			)
		_lowercase :    Dict							=							array
	return tf_weights


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							Path(__UpperCAmelCase			).parent.name
	_lowercase :    Any							=							task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
	_lowercase :    Dict							=							PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,model_max_length=__UpperCAmelCase			)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(__UpperCAmelCase			)

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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.')
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE									=						Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE									=						os.path.join('pegasus', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""







import math


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be an integer'''
		raise TypeError(__UpperCAmelCase			)

	if number < 1:
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be > 0'''
		raise ValueError(__UpperCAmelCase			)
	elif number == 1:
		return 3
	elif number == 2:
		return 5
	else:
		_lowercase :    str							=							int(math.log(number // 3     ,2			)			) + 2

		_lowercase :    Union[str, Any]							=							[3, 5]
		_lowercase :    Optional[int]							=							2
		_lowercase :    List[Any]							=							3
		for block in range(1     ,__UpperCAmelCase			):
			for _ in range(__UpperCAmelCase			):
				proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]			)
				proth_index += 1
			increment *= 2

	return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												SCREAMING_SNAKE_CASE									=						0
												try:
																		SCREAMING_SNAKE_CASE									=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")



