

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[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       lowercase					(	__A :					Union[str, Any]		)				->			Optional[int]:





    '''simple docstring'''




    for pegasus_name, hf_name in PATTERNS:
        snake_case							:		Dict        =		k.replace(__A ,    __A		)
    return k







def       lowercase					(	__A :					dict ,    __A :					dict		)				->			PegasusForConditionalGeneration:





    '''simple docstring'''




    snake_case							:		Dict        =		DEFAULTS.copy()
    cfg_kwargs.update(__A		)
    snake_case							:		int        =		PegasusConfig(**__A		)
    snake_case							:		List[Any]        =		PegasusForConditionalGeneration(__A		)
    snake_case							:		Optional[Any]        =		torch_model.model.state_dict()
    snake_case							:		Optional[int]        =		{}
    for k, v in tf_weights.items():
        snake_case							:		str        =		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:
            snake_case							:		Optional[Any]        =		v.T
        snake_case							:		List[Any]        =		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
    snake_case							:		List[str]        =		torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]		)
    snake_case							:		Optional[Any]        =		mapping["""shared.weight"""]
    snake_case							:		Optional[Any]        =		mapping["""shared.weight"""]
    snake_case							:		Tuple        =		{k: torch.zeros_like(__A		) for k, v in sd.items() if k.endswith("""bias"""		) and k not in mapping}
    mapping.update(**__A		)
    snake_case      ,		snake_case							:		Union[str, Any]        =		torch_model.model.load_state_dict(__A ,    strict=__A		)
    snake_case							:		Union[str, 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       lowercase					(	__A :					int="./ckpt/aeslc/model.ckpt-32000"		)				->			Dict:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		tf.train.list_variables(__A		)
    snake_case							:		Union[str, Any]        =		{}
    snake_case							:		List[str]        =		["""Adafactor""", """global_step"""]
    for name, shape in tqdm(__A ,    desc="""converting tf checkpoint to dict"""		):
        snake_case							:		str        =		any(pat in name for pat in ignore_name		)
        if skip_key:
            continue
        snake_case							:		List[str]        =		tf.train.load_variable(__A ,    __A		)
        snake_case							:		Optional[Any]        =		array
    return tf_weights







def       lowercase					(	__A :					str ,    __A :					str		)				->			Optional[int]:





    '''simple docstring'''




    snake_case							:		Dict        =		Path(__A		).parent.name
    snake_case							:		Dict        =		task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
    snake_case							:		Any        =		PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" ,    model_max_length=__A		)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(__A		)

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










import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

A       :   Optional[int]					= time.time()
A       :   int					= load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

A       :   Optional[Any]					= time.time()
A       :   int					= ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

A       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')



