



"""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


__snake_case							:     List[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     _lowercase       (						__snake_case			)		->     Any:
			for pegasus_name, hf_name in PATTERNS:
						__lowerCAmelCase   :						Dict				= k.replace(__snake_case      ,__snake_case			)
			return k


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


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[Any]:
			# save tokenizer first
			__lowerCAmelCase   :						Dict				= Path(__snake_case			).parent.name
			__lowerCAmelCase   :						int				= task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
			__lowerCAmelCase   :						Tuple				= PegasusTokenizer.from_pretrained("sshleifer/pegasus"      ,model_max_length=__snake_case			)
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(__snake_case			)

			# convert model
			__lowerCAmelCase   :						Any				= get_tf_weights_as_numpy(__snake_case			)
			__lowerCAmelCase   :						int				= task_specific_params[F"""summarization_{dataset}"""]
			if dataset == "large":
						__lowerCAmelCase   :						Dict				= task_specific_params
			__lowerCAmelCase   :						str				= convert_pegasus(__snake_case      ,__snake_case			)
			torch_model.save_pretrained(__snake_case			)
			__lowerCAmelCase   :						List[str]				= torch_model.state_dict()
			sd.pop("model.decoder.embed_positions.weight"			)
			sd.pop("model.encoder.embed_positions.weight"			)
			torch.save(__snake_case      ,Path(__snake_case			) / "pytorch_model.bin"			)


if __name__ == "__main__":
	__snake_case							:     str   =					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							:     Union[str, Any]   =					parser.parse_args()
	if args.save_dir is None:
		__snake_case							:     Optional[int]   =					Path(args.tf_ckpt_path).parent.name
		__snake_case							:     str   =					os.path.join('pegasus', dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     list:
			__lowerCAmelCase   :						List[str]				= len(__snake_case			)
			__lowerCAmelCase   :						Dict				= []
			for i in range(len(__snake_case			) - pat_len + 1			):
						__lowerCAmelCase   :						List[Any]				= True
						for j in range(__snake_case			):
									if s[i + j] != pattern[j]:
												__lowerCAmelCase   :						Union[str, Any]				= False
												break
						if match_found:
									position.append(__snake_case			)
			return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))