
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


lowercase__	:Optional[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  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''


					for pegasus_name, hf_name in PATTERNS:
										lowercase					=		k.replace(lowerCAmelCase__				,    lowerCAmelCase__	)
					return k



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



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

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


if __name__ == "__main__":
							lowercase__	:List[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.")
							lowercase__	:Dict										=					parser.parse_args()
							if args.save_dir is None:
														lowercase__	:Dict										=					Path(args.tf_ckpt_path).parent.name
														lowercase__	:Optional[Any]										=					os.path.join("pegasus", dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






import logging

from transformers import PretrainedConfig


lowercase__	:int										=					logging.getLogger(__name__)


lowercase__	:Dict										=					{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       ='''bertabs'''




							def __init__(       self     ,A__=3_0_5_2_2     ,A__=5_1_2     ,A__=6     ,A__=5_1_2     ,A__=8     ,A__=5_1_2     ,A__=0.2     ,A__=6     ,A__=7_6_8     ,A__=8     ,A__=2_0_4_8     ,A__=0.2     ,**A__     ,):
												super().__init__(**A__)

												lowercase					=		vocab_size
												lowercase					=		max_pos

												lowercase					=		enc_layers
												lowercase					=		enc_hidden_size
												lowercase					=		enc_heads
												lowercase					=		enc_ff_size
												lowercase					=		enc_dropout

												lowercase					=		dec_layers
												lowercase					=		dec_hidden_size
												lowercase					=		dec_heads
												lowercase					=		dec_ff_size
												lowercase					=		dec_dropout




