






"""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						:						int					   =       [
    # 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					(					lowerCAmelCase__				):






				for pegasus_name, hf_name in PATTERNS:
								A__							 =       k.replace(a__       ,a__				)
				return k



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       DEFAULTS.copy()
				cfg_kwargs.update(a__				)
				A__							 =       PegasusConfig(**a__				)
				A__							 =       PegasusForConditionalGeneration(a__				)
				A__							 =       torch_model.model.state_dict()
				A__							 =       {}
				for k, v in tf_weights.items():
								A__							 =       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:
												A__							 =       v.T
								A__							 =       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
				A__							 =       torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]				)
				A__							 =       mapping['shared.weight']
				A__							 =       mapping['shared.weight']
				A__							 =       {k: torch.zeros_like(a__				) for k, v in sd.items() if k.endswith('bias'				) and k not in mapping}
				mapping.update(**a__				)
				A__ ,			A__							 =       torch_model.model.load_state_dict(a__       ,strict=a__				)
				A__							 =       [
				    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					(					lowerCAmelCase__="./ckpt/aeslc/model.ckpt-32000"				):
				A__							 =       tf.train.list_variables(a__				)
				A__							 =       {}
				A__							 =       ['Adafactor', 'global_step']
				for name, shape in tqdm(a__       ,desc='converting tf checkpoint to dict'				):
								A__							 =       any(pat in name for pat in ignore_name				)
								if skip_key:
												continue
								A__							 =       tf.train.load_variable(a__       ,a__				)
								A__							 =       array
				return tf_weights



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       Path(a__				).parent.name
				A__							 =       task_specific_params[f'''summarization_{dataset}''']['max_position_embeddings']
				A__							 =       PegasusTokenizer.from_pretrained('sshleifer/pegasus'       ,model_max_length=a__				)
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(a__				)

				# convert model
				A__							 =       get_tf_weights_as_numpy(a__				)
				A__							 =       task_specific_params[f'''summarization_{dataset}''']
				if dataset == "large":
								A__							 =       task_specific_params
				A__							 =       convert_pegasus(a__       ,a__				)
				torch_model.save_pretrained(a__				)
				A__							 =       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__":
					SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       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						:						List[Any]					   =       parser.parse_args()
					if args.save_dir is None:
										SCREAMING_SNAKE_CASE						:						List[Any]					   =       Path(args.tf_ckpt_path).parent.name
										SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       os.path.join('''pegasus''', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)










"""simple docstring"""






from maths.prime_check import is_prime



def  __lowerCamelCase					(					lowerCAmelCase__				):
				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       f'''Input value of [number={number}] must be an integer'''
								raise TypeError(lowerCAmelCase__				)
				if is_prime(lowerCAmelCase__				) and is_prime(number + 2				):
								return number + 2
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()


