





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


A_												=     [
    # 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      (    a) ->Dict:
					for pegasus_name, hf_name in PATTERNS:
										lowerCamelCase__       =       k.replace(a,     a)
					return k





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





def    __UpperCamelCase      (    a,     a) ->Dict:
					# save tokenizer first
					lowerCamelCase__       =       Path(a).parent.name
					lowerCamelCase__       =       task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
					lowerCamelCase__       =       PegasusTokenizer.from_pretrained("sshleifer/pegasus",     model_max_length=a)
					assert tok.model_max_length == desired_max_model_length
					tok.save_pretrained(a)

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








def    __UpperCamelCase      (    a = 100) ->int:
					lowerCamelCase__       =       (n * (n + 1) // 2) ** 2
					lowerCamelCase__       =       n * (n + 1) * (2 * n + 1) // 6
					return sum_cubes - sum_squares


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

