


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


_snake_case     :		Tuple						=				[
    # 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:
														__snake_case		:			int							 =   k.replace(UpperCamelCase__							,	UpperCamelCase__	)
							return k







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







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			List[str]							 =   Path(UpperCamelCase__	).parent.name
							__snake_case		:			List[Any]							 =   task_specific_params[F'summarization_{dataset}']["max_position_embeddings"]
							__snake_case		:			Optional[Any]							 =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,	model_max_length=UpperCamelCase__	)
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(UpperCamelCase__	)

							# convert model
							__snake_case		:			List[Any]							 =   get_tf_weights_as_numpy(UpperCamelCase__	)
							__snake_case		:			List[str]							 =   task_specific_params[F'summarization_{dataset}']
							if dataset == "large":
														__snake_case		:			List[str]							 =   task_specific_params
							__snake_case		:			Any							 =   convert_pegasus(UpperCamelCase__							,	UpperCamelCase__	)
							torch_model.save_pretrained(UpperCamelCase__	)
							__snake_case		:			int							 =   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__":
			_snake_case     :		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.")
			_snake_case     :		List[str]						=				parser.parse_args()
			if args.save_dir is None:
						_snake_case     :		str						=				Path(args.tf_ckpt_path).parent.name
						_snake_case     :		List[Any]						=				os.path.join("pegasus", dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




