

def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase = 600_851_475_143					)       ->			int:



    '''simple docstring'''






    try:
        lowerCAmelCase					:      str           =   int(_UpperCAmelCase					)
    except (TypeError, ValueError):
        raise TypeError('Parameter n must be int or castable to int.'					)
    if n <= 0:
        raise ValueError('Parameter n must be greater than or equal to one.'					)
    lowerCAmelCase					:      Union[str, Any]           =   1
    lowerCAmelCase					:      List[Any]           =   2
    while i * i <= n:
        while n % i == 0:
            lowerCAmelCase					:      Any           =   i
            n //= i
        i += 1
    if n > 1:
        lowerCAmelCase					:      List[str]           =   n
    return int(_UpperCAmelCase					)


if __name__ == "__main__":
 print(F'{solution() = }')









'''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)


